Pub Date : 2024-04-08DOI: 10.1109/TAI.2024.3386149
Handuo Yang;Ju Huyan;Tao Ma;Yitao Song;Chengjia Han
To address two key challenges—limited grid-level detection capability and difficulty in detecting pavement cracks in complex environments, this study proposes a novel neural network model called CrackcellNet. This innovative model incorporates an output structure that enables end-to-end grid recognition and a module that enhances shadow image data to enhance crack detection. The model relies on the design of consecutive pooling layers to achieve adaptive target size grid output. By utilizing image fusion techniques, it enhances the quantity of shadow data in road surface detection. The results of ablation experiments indicate that the optimal configuration for CrackcellNet includes V-block and shadow augmentation operations, dilation rates of 1 or 2, and a convolutional layer in the CBA module. Through extensive experimentation, we have demonstrated that our model achieved an accuracy rate of 94.5% for grid-level crack detection and a F1 value of 0.839. Furthermore, practical engineering validation confirms the model's efficacy with an average PCIe of 0.045, providing valuable guidance for road maintenance decisions.
为了解决网格级检测能力有限和复杂环境下路面裂缝检测困难这两大难题，本研究提出了一种名为 CrackcellNet 的新型神经网络模型。这一创新模型包含一个可实现端到端网格识别的输出结构和一个可增强阴影图像数据以提高裂缝检测能力的模块。该模型依靠连续池化层的设计来实现自适应目标尺寸网格输出。通过利用图像融合技术，该模型增强了路面检测中阴影数据的数量。烧蚀实验结果表明，CrackcellNet 的最佳配置包括 V 块和阴影增强操作、1 或 2 的扩张率以及 CBA 模块中的卷积层。通过大量实验，我们证明了我们的模型在网格级裂纹检测方面达到了 94.5% 的准确率和 0.839 的 F1 值。此外，实际工程验证也证实了该模型的有效性，其平均 PCIe 为 0.045，为道路维护决策提供了宝贵的指导。
{"title":"A Novel Applicable Shadow Resistant Neural Network Model for High-Efficiency Grid-Level Pavement Crack Detection","authors":"Handuo Yang;Ju Huyan;Tao Ma;Yitao Song;Chengjia Han","doi":"10.1109/TAI.2024.3386149","DOIUrl":"https://doi.org/10.1109/TAI.2024.3386149","url":null,"abstract":"To address two key challenges—limited grid-level detection capability and difficulty in detecting pavement cracks in complex environments, this study proposes a novel neural network model called CrackcellNet. This innovative model incorporates an output structure that enables end-to-end grid recognition and a module that enhances shadow image data to enhance crack detection. The model relies on the design of consecutive pooling layers to achieve adaptive target size grid output. By utilizing image fusion techniques, it enhances the quantity of shadow data in road surface detection. The results of ablation experiments indicate that the optimal configuration for CrackcellNet includes V-block and shadow augmentation operations, dilation rates of 1 or 2, and a convolutional layer in the CBA module. Through extensive experimentation, we have demonstrated that our model achieved an accuracy rate of 94.5% for grid-level crack detection and a F1 value of 0.839. Furthermore, practical engineering validation confirms the model's efficacy with an average PCIe of 0.045, providing valuable guidance for road maintenance decisions.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4535-4549"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.1109/TAI.2024.3385384
Guifeng Ye;Shaowen Lu
Our proposed algorithm is a prefetching-based multiproposal Markov Chain Monte Carlo (PMP-MCMC) method that efficiently explores the target distribution by combining multiple proposals with the concept of prefetching. In our method, not all proposals are directly derived from the current state; some are derived from future states. This approach breaks through the inherent sequential characteristics of traditional MCMC algorithms. Compared with single-proposal and multiproposal methods, our approach speeds up by $K$ times and the burn-in period is reduced by a factor of $1/text{log}_{2}K$ maximally, where $K$ is the number of parallel computational units or processing cores. Compared with prefetching method, our method has increased the number of samples per iteration by a factor of $K/text{log}_{2}K$ . Furthermore, the proposed method is general and can be integrated into MCMC variants such as Hamiltonian Monte Carlo (HMC). We have also applied this method to optimize the model parameters of neural networks and Bayesian inference and observed significant improvements in optimization performance.
{"title":"Prefetching-based Multiproposal Markov Chain Monte Carlo Algorithm","authors":"Guifeng Ye;Shaowen Lu","doi":"10.1109/TAI.2024.3385384","DOIUrl":"https://doi.org/10.1109/TAI.2024.3385384","url":null,"abstract":"Our proposed algorithm is a prefetching-based multiproposal Markov Chain Monte Carlo (PMP-MCMC) method that efficiently explores the target distribution by combining multiple proposals with the concept of prefetching. In our method, not all proposals are directly derived from the current state; some are derived from future states. This approach breaks through the inherent sequential characteristics of traditional MCMC algorithms. Compared with single-proposal and multiproposal methods, our approach speeds up by \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 times and the burn-in period is reduced by a factor of \u0000<inline-formula><tex-math>$1/text{log}_{2}K$</tex-math></inline-formula>\u0000 maximally, where \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 is the number of parallel computational units or processing cores. Compared with prefetching method, our method has increased the number of samples per iteration by a factor of \u0000<inline-formula><tex-math>$K/text{log}_{2}K$</tex-math></inline-formula>\u0000. Furthermore, the proposed method is general and can be integrated into MCMC variants such as Hamiltonian Monte Carlo (HMC). We have also applied this method to optimize the model parameters of neural networks and Bayesian inference and observed significant improvements in optimization performance.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4493-4505"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.1109/TAI.2024.3385390
Tiehang Duan;Zhenyi Wang;Li Shen;Gianfranco Doretto;Donald A. Adjeroh;Fang Li;Cui Tao
Large variance exists in Electroencephalogram (EEG) signals with its pattern differing significantly across subjects. It is a challenging problem to perform online sequential decoding of EEG signals across different subjects, where a sequence of subjects arrive in temporal order and no signal data is jointly available beforehand. The challenges include the following two aspects: 1) the knowledge learned from previous subjects does not readily fit to future subjects, and fast adaptation is needed in the process; and 2) the EEG classifier could drastically erase information of learnt subjects as learning progresses, namely catastrophic forgetting. Most existing EEG decoding explorations use sizable data for pretraining purposes, and to the best of our knowledge we are the first to tackle this challenging online sequential decoding setting. In this work, we propose a unified bi-level meta-learning framework that enables the EEG decoder to simultaneously perform fast adaptation on future subjects and retain knowledge of previous subjects. In addition, we extend to the more general subject-agnostic scenario and propose a subject shift detection algorithm for situations that subject identity and the occurrence of subject shifts are unknown. We conducted experiments on three public EEG datasets for both subject-aware and subject-agnostic scenarios. The proposed method demonstrates its effectiveness in most of the ablation settings, e.g. an improvement of 5.73% for forgetting mitigation and 3.50% for forward adaptation on SEED dataset for subject agnostic scenarios.
{"title":"Retain and Adapt: Online Sequential EEG Classification With Subject Shift","authors":"Tiehang Duan;Zhenyi Wang;Li Shen;Gianfranco Doretto;Donald A. Adjeroh;Fang Li;Cui Tao","doi":"10.1109/TAI.2024.3385390","DOIUrl":"https://doi.org/10.1109/TAI.2024.3385390","url":null,"abstract":"Large variance exists in Electroencephalogram (EEG) signals with its pattern differing significantly across subjects. It is a challenging problem to perform online sequential decoding of EEG signals across different subjects, where a sequence of subjects arrive in temporal order and no signal data is jointly available beforehand. The challenges include the following two aspects: 1) the knowledge learned from previous subjects does not readily fit to future subjects, and fast adaptation is needed in the process; and 2) the EEG classifier could drastically erase information of learnt subjects as learning progresses, namely catastrophic forgetting. Most existing EEG decoding explorations use sizable data for pretraining purposes, and to the best of our knowledge we are the first to tackle this challenging online sequential decoding setting. In this work, we propose a unified bi-level meta-learning framework that enables the EEG decoder to simultaneously perform fast adaptation on future subjects and retain knowledge of previous subjects. In addition, we extend to the more general subject-agnostic scenario and propose a subject shift detection algorithm for situations that subject identity and the occurrence of subject shifts are unknown. We conducted experiments on three public EEG datasets for both subject-aware and subject-agnostic scenarios. The proposed method demonstrates its effectiveness in most of the ablation settings, e.g. an improvement of 5.73% for forgetting mitigation and 3.50% for forward adaptation on SEED dataset for subject agnostic scenarios.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4479-4492"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.1109/TAI.2024.3385387
Tingkai Chen;Ning Wang
In this article, to holistically tackle underwater detection degradation due to unknown geometric variation arising from scale, pose, viewpoint, and occlusion under low-contrast and color-distortion circumstances, a shuffled grouping cross-channel attention-based bilateral-filter-interpolation deformable ConvNet (SGCA-BDC) framework is established for benthonic organism detection (BOD). Main contributions are as follows: 1) By comprehensively considering spatial and feature similarities between offset and integral coordinate positions, the BDC with modulation weight mechanism is created, such that sampling ability of convolutional kernel for BO with unknown geometric variation can be adaptively augmented from spatial perspective; 2) By utilizing 1-D convolution to recalibrate channel weight for grouped subfeature via information entropy statistic technique, an SGCA module is innovated, such that seabed background noise can be suppressed from channel aspect; 3) The proposed SGCA-BDC scheme is eventually built in an organic manner by incorporating BDC and SGCA modules. Comprehensive experiments and comparisons demonstrate that the SGCA-BDC scheme remarkably outperforms typical detection approaches including Faster RCNN, SSD, YOLOv6, YOLOv7, YOLOv8, RetinaNet, and CenterNet in terms of mean average precision by 8.54%, 4.4%, 5.18%, 3.1%, 3.01%, 12.53%, and 7.09%, respectively.
{"title":"Shuffled Grouping Cross-Channel Attention-Based Bilateral-Filter-Interpolation Deformable ConvNet With Applications to Benthonic Organism Detection","authors":"Tingkai Chen;Ning Wang","doi":"10.1109/TAI.2024.3385387","DOIUrl":"https://doi.org/10.1109/TAI.2024.3385387","url":null,"abstract":"In this article, to holistically tackle underwater detection degradation due to unknown geometric variation arising from scale, pose, viewpoint, and occlusion under low-contrast and color-distortion circumstances, a shuffled grouping cross-channel attention-based bilateral-filter-interpolation deformable ConvNet (SGCA-BDC) framework is established for benthonic organism detection (BOD). Main contributions are as follows: 1) By comprehensively considering spatial and feature similarities between offset and integral coordinate positions, the BDC with modulation weight mechanism is created, such that sampling ability of convolutional kernel for BO with unknown geometric variation can be adaptively augmented from spatial perspective; 2) By utilizing 1-D convolution to recalibrate channel weight for grouped subfeature via information entropy statistic technique, an SGCA module is innovated, such that seabed background noise can be suppressed from channel aspect; 3) The proposed SGCA-BDC scheme is eventually built in an organic manner by incorporating BDC and SGCA modules. Comprehensive experiments and comparisons demonstrate that the SGCA-BDC scheme remarkably outperforms typical detection approaches including Faster RCNN, SSD, YOLOv6, YOLOv7, YOLOv8, RetinaNet, and CenterNet in terms of mean average precision by 8.54%, 4.4%, 5.18%, 3.1%, 3.01%, 12.53%, and 7.09%, respectively.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4506-4518"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.1109/TAI.2024.3385386
Zongwen Fan;Jin Gou;Shaoyuan Weng
Concrete is a vital component in modern construction, prized for its strength, durability, and versatility. Accurately determining the quantities of concrete components is crucial in civil engineering applications to optimize resources (e.g., manpower and financial resources). In this article, we propose an unbiased fuzzy-weighted relative error support vector machine (UFW-RE-SVM) for reverse prediction of concrete components. First, we add an unbiased term to the target function of UFW-RE-SVM for obtaining an unbiased model. Second, we design a fuzzy-weighted operation to indicate sample importance by incorporating the fuzzy membership values into the UFW-RE-SVM. The $n$ th root operation is introduced to address the exponential explosion issue in the fuzzy-weighted operation. Finally, considering the UFW-RE-SVM is sensitive to its hyperparameters for multioutput prediction, the whale optimization algorithm (WOA) is utilized for hyperparameter optimization for its effectiveness in optimization tasks. We design the fitness function based on the results from multiple components to balance the performance of multioutput predictions. Experimental results show that the performance of our proposed model outperforms existing works in predicting concrete components in terms of mean absolute relative error, standard deviation, and root mean square error. Further, the statistical test shows the WOA and two other metaheuristics can significantly improve the prediction performance. This indicates that the unbiased term, fuzzy-weighted operation, and WOA are effective for improving the proposed model for reverse prediction concrete components. With these promising results, the proposed model could provide decision-makers with a valuable tool for determining concrete component quantities based on desired concrete qualities.
{"title":"An Unbiased Fuzzy Weighted Relative Error Support Vector Machine for Reverse Prediction of Concrete Components","authors":"Zongwen Fan;Jin Gou;Shaoyuan Weng","doi":"10.1109/TAI.2024.3385386","DOIUrl":"https://doi.org/10.1109/TAI.2024.3385386","url":null,"abstract":"Concrete is a vital component in modern construction, prized for its strength, durability, and versatility. Accurately determining the quantities of concrete components is crucial in civil engineering applications to optimize resources (e.g., manpower and financial resources). In this article, we propose an unbiased fuzzy-weighted relative error support vector machine (UFW-RE-SVM) for reverse prediction of concrete components. First, we add an unbiased term to the target function of UFW-RE-SVM for obtaining an unbiased model. Second, we design a fuzzy-weighted operation to indicate sample importance by incorporating the fuzzy membership values into the UFW-RE-SVM. The \u0000<inline-formula><tex-math>$n$</tex-math></inline-formula>\u0000th root operation is introduced to address the exponential explosion issue in the fuzzy-weighted operation. Finally, considering the UFW-RE-SVM is sensitive to its hyperparameters for multioutput prediction, the whale optimization algorithm (WOA) is utilized for hyperparameter optimization for its effectiveness in optimization tasks. We design the fitness function based on the results from multiple components to balance the performance of multioutput predictions. Experimental results show that the performance of our proposed model outperforms existing works in predicting concrete components in terms of mean absolute relative error, standard deviation, and root mean square error. Further, the statistical test shows the WOA and two other metaheuristics can significantly improve the prediction performance. This indicates that the unbiased term, fuzzy-weighted operation, and WOA are effective for improving the proposed model for reverse prediction concrete components. With these promising results, the proposed model could provide decision-makers with a valuable tool for determining concrete component quantities based on desired concrete qualities.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4574-4584"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.1109/TAI.2024.3385743
Huilin Deng;Hongchen Luo;Wei Zhai;Yanming Guo;Yang Cao;Yu Kang
In response to the rapid evolution of products in industrial inspection, this article introduces the cross-category few-shot anomaly detection (C-FSAD) task, aimed at efficiently detecting anomalies in new object categories with minimal normal samples. However, the diversity of defects and significant visual distinctions among various objects hinder the identification of anomalous regions. To tackle this, we adopt a pairwise comparison between query and normal samples, establishing an intimate correlation through fine-grained correspondence. Specifically, we propose the prioritized local matching network (PLMNet), emphasizing local analysis of correlation, which includes three primary components: 1) Local perception network refines the initial matches through bidirectional local analysis; 2) step aggregation strategy employs multiple stages of local convolutional pooling to aggregate local insights; and 3) defect-sensitive Weight Learner adaptively enhances channels informative for defect structures, ensuring more discriminative representations of encoded context. Our PLMNet deepens the interpretation of correlations, from geometric cues to semantics, efficiently extracting discrepancies in feature space. Extensive experiments on two standard industrial anomaly detection benchmarks demonstrate our state-of-the-art performance in both detection and localization, with margins of 9.8% and 5.4%, respectively.
{"title":"Prioritized Local Matching Network for Cross-Category Few-Shot Anomaly Detection","authors":"Huilin Deng;Hongchen Luo;Wei Zhai;Yanming Guo;Yang Cao;Yu Kang","doi":"10.1109/TAI.2024.3385743","DOIUrl":"https://doi.org/10.1109/TAI.2024.3385743","url":null,"abstract":"In response to the rapid evolution of products in industrial inspection, this article introduces the cross-category few-shot anomaly detection (C-FSAD) task, aimed at efficiently detecting anomalies in new object categories with minimal normal samples. However, the diversity of defects and significant visual distinctions among various objects hinder the identification of anomalous regions. To tackle this, we adopt a pairwise comparison between query and normal samples, establishing an intimate correlation through fine-grained correspondence. Specifically, we propose the prioritized local matching network (PLMNet), emphasizing local analysis of correlation, which includes three primary components: 1) Local perception network refines the initial matches through bidirectional local analysis; 2) step aggregation strategy employs multiple stages of local convolutional pooling to aggregate local insights; and 3) defect-sensitive Weight Learner adaptively enhances channels informative for defect structures, ensuring more discriminative representations of encoded context. Our PLMNet deepens the interpretation of correlations, from geometric cues to semantics, efficiently extracting discrepancies in feature space. Extensive experiments on two standard industrial anomaly detection benchmarks demonstrate our state-of-the-art performance in both detection and localization, with margins of 9.8% and 5.4%, respectively.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4550-4561"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.1109/TAI.2024.3384938
Iram Arshad;Saeed Hamood Alsamhi;Yuansong Qiao;Brian Lee;Yuhang Ye
Deep neural networks are susceptible to various backdoor attacks, such as training time attacks, where the attacker can inject a trigger pattern into a small portion of the dataset to control the model's predictions at runtime. Backdoor attacks are dangerous because they do not degrade the model's performance. This article explores the feasibility of a new type of backdoor attack, a data-free backdoor. Unlike traditional backdoor attacks that require poisoning data and injection during training, our approach, the iterative optimization trigger method (IOTM), enables trigger generation without compromising the integrity of the models and datasets. We propose an attack based on an IOTM technique, guided by an adaptive trigger generator (ATG) and employing a custom objective function. ATG dynamically refines the trigger using feedback from the model's predictions. We empirically evaluated the effectiveness of IOTM with three deep learning models (CNN, VGG16, and ResNet18) using the CIFAR10 dataset. The achieved runtime-attack success rate (R-ASR) varies across different classes. For some classes, the R-ASR reached 100%; whereas, for others, it reached 62%. Furthermore, we conducted an ablation study to investigate critical factors in the runtime backdoor, including optimizer, weight, “REG,” and trigger visibility on R-ASR using the CIFAR100 dataset. We observed significant variations in the R-ASR by changing the optimizer, including Adam and SGD, with and without momentum. The R-ASR reached 81.25% with the Adam optimizer, whereas the SGD with momentum and without results reached 46.87% and 3.12%, respectively.
{"title":"IOTM: Iterative Optimization Trigger Method—A Runtime Data-Free Backdoor Attacks on Deep Neural Networks","authors":"Iram Arshad;Saeed Hamood Alsamhi;Yuansong Qiao;Brian Lee;Yuhang Ye","doi":"10.1109/TAI.2024.3384938","DOIUrl":"https://doi.org/10.1109/TAI.2024.3384938","url":null,"abstract":"Deep neural networks are susceptible to various backdoor attacks, such as training time attacks, where the attacker can inject a trigger pattern into a small portion of the dataset to control the model's predictions at runtime. Backdoor attacks are dangerous because they do not degrade the model's performance. This article explores the feasibility of a new type of backdoor attack, a \u0000<italic>data-free</i>\u0000 backdoor. Unlike traditional backdoor attacks that require poisoning data and injection during training, our approach, the iterative optimization trigger method (IOTM), enables trigger generation without compromising the integrity of the models and datasets. We propose an attack based on an IOTM technique, guided by an adaptive trigger generator (ATG) and employing a custom objective function. ATG dynamically refines the trigger using feedback from the model's predictions. We empirically evaluated the effectiveness of IOTM with three deep learning models (CNN, VGG16, and ResNet18) using the CIFAR10 dataset. The achieved runtime-attack success rate (R-ASR) varies across different classes. For some classes, the R-ASR reached 100%; whereas, for others, it reached 62%. Furthermore, we conducted an ablation study to investigate critical factors in the runtime backdoor, including optimizer, weight, “REG,” and trigger visibility on R-ASR using the CIFAR100 dataset. We observed significant variations in the R-ASR by changing the optimizer, including Adam and SGD, with and without momentum. The R-ASR reached 81.25% with the Adam optimizer, whereas the SGD with momentum and without results reached 46.87% and 3.12%, respectively.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4562-4573"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.1109/TAI.2024.3384337
Rachel Selva Dhanaraj;M. Sridevi
Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets—CelebA, CIFAR-10, and MNIST—were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss.
{"title":"Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack","authors":"Rachel Selva Dhanaraj;M. Sridevi","doi":"10.1109/TAI.2024.3384337","DOIUrl":"https://doi.org/10.1109/TAI.2024.3384337","url":null,"abstract":"Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets—CelebA, CIFAR-10, and MNIST—were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4470-4478"},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep neural networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples—input samples that have been perturbed to force DNN-based models to make errors. As a result, adversarial machine learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in antisocial AI applications. The emergence of new AI technologies that leverage large language models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing antisocial applications at scale. AdvML for social good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent prosocial applications. Regulators, practitioners, and researchers should collaborate to encourage the development of prosocial applications and hinder the development of antisocial ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating prosocial applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community.
{"title":"Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally","authors":"Shawqi Al-Maliki;Adnan Qayyum;Hassan Ali;Mohamed Abdallah;Junaid Qadir;Dinh Thai Hoang;Dusit Niyato;Ala Al-Fuqaha","doi":"10.1109/TAI.2024.3383407","DOIUrl":"https://doi.org/10.1109/TAI.2024.3383407","url":null,"abstract":"Deep neural networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples—input samples that have been perturbed to force DNN-based models to make errors. As a result, adversarial machine learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in antisocial AI applications. The emergence of new AI technologies that leverage large language models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing antisocial applications at scale. AdvML for social good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent prosocial applications. Regulators, practitioners, and researchers should collaborate to encourage the development of prosocial applications and hinder the development of antisocial ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating prosocial applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4322-4343"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-31DOI: 10.1109/TAI.2024.3407033
Wenxuan Pan;Feifei Zhao;Zhuoya Zhao;Yi Zeng
The intricate and distinctive evolutionary topology of the human brain enables it to execute multiple cognitive tasks simultaneously, and this automated evolutionary process of biological networks motivates our investigation into efficient architecture optimization for spiking neural networks (SNNs). Diverging from traditional manual-designed and hierarchical network architecture search (NAS), we advance the evolution of SNN architecture by integrating local, brain region-inspired modular structures with global cross-module connectivity. Locally, the brain region-inspired module consists of multiple neural motifs with excitatory and inhibitory connections; globally, free connections among modules, including long-term cross-module feedforward and feedback connections are evolved. We introduce an efficient multiobjective evolutionary algorithm that leverages a few-shot predictor, endowing SNNs with high performance and low energy consumption. Extensive experiments across both static (CIFAR10, CIFAR100) and neuromorphic (CIFAR10-DVS, DVS128-Gesture) datasets reveal that the proposed model significantly exhibits robustness while maintaining consistent and exceptional performance. This study pioneers in searching for optimal neural architectures for SNNs by integrating the human brain's advanced connectivity and modular organization into SNN optimization, thereby contributing valuable perspectives to the development of brain-inspired artificial intelligence.
{"title":"Brain-Inspired Evolutionary Architectures for Spiking Neural Networks","authors":"Wenxuan Pan;Feifei Zhao;Zhuoya Zhao;Yi Zeng","doi":"10.1109/TAI.2024.3407033","DOIUrl":"https://doi.org/10.1109/TAI.2024.3407033","url":null,"abstract":"The intricate and distinctive evolutionary topology of the human brain enables it to execute multiple cognitive tasks simultaneously, and this automated evolutionary process of biological networks motivates our investigation into efficient architecture optimization for spiking neural networks (SNNs). Diverging from traditional manual-designed and hierarchical network architecture search (NAS), we advance the evolution of SNN architecture by integrating local, brain region-inspired modular structures with global cross-module connectivity. Locally, the brain region-inspired module consists of multiple neural motifs with excitatory and inhibitory connections; globally, free connections among modules, including long-term cross-module feedforward and feedback connections are evolved. We introduce an efficient multiobjective evolutionary algorithm that leverages a few-shot predictor, endowing SNNs with high performance and low energy consumption. Extensive experiments across both static (CIFAR10, CIFAR100) and neuromorphic (CIFAR10-DVS, DVS128-Gesture) datasets reveal that the proposed model significantly exhibits robustness while maintaining consistent and exceptional performance. This study pioneers in searching for optimal neural architectures for SNNs by integrating the human brain's advanced connectivity and modular organization into SNN optimization, thereby contributing valuable perspectives to the development of brain-inspired artificial intelligence.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5760-5770"},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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