Pub Date : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648386
Zhenyu Li, Daofu Gong, Lei Tan, Xiangyang Luo, Fenlin Liu, A. Bors
3D printing is faced with a lot of security issues, such as malicious tampering, intellectual property theft and so on. This work aims to protect the G-code file which controls the 3D printing process by proposing a self-embedding watermarking method for G-code file. This method groups the G-code lines into code blocks and achieves a random mapping relationship for each code block. Each code block is divided into two parts, carrying the authentication and recovery bits, respectively. The tampered regions are detected by leveraging the authentication bits in each code block. Meanwhile, the G-code files are restored based on the recovery bits and the geometric information of the neighboring code blocks. Experimental results indicate that the proposed method can effectively detect the tampered region and restore the G-code file to a large extent, while limiting the distortion caused to the 3D printed object by the watermarking.
{"title":"Self-embedding watermarking method for G-code used in 3D printing","authors":"Zhenyu Li, Daofu Gong, Lei Tan, Xiangyang Luo, Fenlin Liu, A. Bors","doi":"10.1109/WIFS53200.2021.9648386","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648386","url":null,"abstract":"3D printing is faced with a lot of security issues, such as malicious tampering, intellectual property theft and so on. This work aims to protect the G-code file which controls the 3D printing process by proposing a self-embedding watermarking method for G-code file. This method groups the G-code lines into code blocks and achieves a random mapping relationship for each code block. Each code block is divided into two parts, carrying the authentication and recovery bits, respectively. The tampered regions are detected by leveraging the authentication bits in each code block. Meanwhile, the G-code files are restored based on the recovery bits and the geometric information of the neighboring code blocks. Experimental results indicate that the proposed method can effectively detect the tampered region and restore the G-code file to a large extent, while limiting the distortion caused to the 3D printed object by the watermarking.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128835360","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648397
Rony Abecidan, V. Itier, Jérémie Boulanger, P. Bas
Domain adaptation is a major issue for doing practical forensics. Since examined images are likely to come from a different development pipeline compared to the ones used for training our models, that may disturb them by a lot, degrading their performances. In this paper, we present a method enabling to make a forgery detector more robust to distributions different but related to its training one, inspired by [1]. The strategy exhibited in this paper foster a detector to find a feature invariant space where source and target distributions are close. Our study deals more precisely with discrepancies observed due to JPEG compressions and our experiments reveal that the proposed adaptation scheme can reasonably reduce the mismatch, even with a rather small target set with no labels when the source domain is properly selected. On top of that, when a small portion of labelled target images is available this method reduces the gap with mix training while being unsupervised.
{"title":"Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics","authors":"Rony Abecidan, V. Itier, Jérémie Boulanger, P. Bas","doi":"10.1109/WIFS53200.2021.9648397","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648397","url":null,"abstract":"Domain adaptation is a major issue for doing practical forensics. Since examined images are likely to come from a different development pipeline compared to the ones used for training our models, that may disturb them by a lot, degrading their performances. In this paper, we present a method enabling to make a forgery detector more robust to distributions different but related to its training one, inspired by [1]. The strategy exhibited in this paper foster a detector to find a feature invariant space where source and target distributions are close. Our study deals more precisely with discrepancies observed due to JPEG compressions and our experiments reveal that the proposed adaptation scheme can reasonably reduce the mismatch, even with a rather small target set with no labels when the source domain is properly selected. On top of that, when a small portion of labelled target images is available this method reduces the gap with mix training while being unsupervised.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115135709","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648380
Kaiyu Ying, Rangding Wang, Diqun Yan
Recent studies have shown that adversarial examples can easily deceive neural networks. But how to ensure the accuracy of extraction while introducing perturbations to steganography is a major difficulty. In this paper, we propose a method of iterative adversarial stego post-processing model called IA-SPP that can generate enhanced post-stego audio to resist steganalysis networks and the SPL of adversarial perturbations is restricted. The model decomposes the perturbation to the point level and updates point-wise perturbations iteratively by the large-absolute-gradient-first rule. The enhanced post-stego obtained by adding the stego and the adversarial perturbation has a high probability of being judged as a cover by the target network. In particular, We further considered how to simultaneously fight against multiple networks. The extensive experiments on the TIMIT show that the proposed model generalizes well across different steganography methods.
{"title":"Iteratively Generated Adversarial Perturbation for Audio Stego Post-processing","authors":"Kaiyu Ying, Rangding Wang, Diqun Yan","doi":"10.1109/WIFS53200.2021.9648380","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648380","url":null,"abstract":"Recent studies have shown that adversarial examples can easily deceive neural networks. But how to ensure the accuracy of extraction while introducing perturbations to steganography is a major difficulty. In this paper, we propose a method of iterative adversarial stego post-processing model called IA-SPP that can generate enhanced post-stego audio to resist steganalysis networks and the SPL of adversarial perturbations is restricted. The model decomposes the perturbation to the point level and updates point-wise perturbations iteratively by the large-absolute-gradient-first rule. The enhanced post-stego obtained by adding the stego and the adversarial perturbation has a high probability of being judged as a cover by the target network. In particular, We further considered how to simultaneously fight against multiple networks. The extensive experiments on the TIMIT show that the proposed model generalizes well across different steganography methods.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129120268","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648384
Elyes Khermaza, Iuliia Tkachenko, J. Picard
Copy Detection Patterns (CDP) have received significant attention from academia and industry as a practical mean of detecting counterfeits. Their security level against sophisticated attacks has been studied theoretically and practically in different research papers, but for reasons that will be explained below, the results are not fully conclusive. In addition, the publicly available CDP datasets are not practically usable to evaluate the performance of authentication algorithms. In short, the apparently simple question: “are copy detection patterns secure against copy?”, remains unanswered as of today. The primary contribution of this paper is to present a publicly available dataset of CDPs including multiple types of copies and attacks, allowing to systematically compare the performance level of CDPs against different attacks proposed in the prior art. The specific case in which a CDP is the same for an entire batch of prints, which is of practical importance as it covers applications with widely used industrial printers such as offset, flexo and rotogravure, is also studied. A second contribution is to highlight the role played by the CDP detector and its different processing steps. Indeed, depending on the specific processing involved, the detection performance can widely outperform the CDP bit error rate which has been used as a reference metrics in the prior art.
{"title":"Can Copy Detection Patterns be copied? Evaluating the performance of attacks and highlighting the role of the detector","authors":"Elyes Khermaza, Iuliia Tkachenko, J. Picard","doi":"10.1109/WIFS53200.2021.9648384","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648384","url":null,"abstract":"Copy Detection Patterns (CDP) have received significant attention from academia and industry as a practical mean of detecting counterfeits. Their security level against sophisticated attacks has been studied theoretically and practically in different research papers, but for reasons that will be explained below, the results are not fully conclusive. In addition, the publicly available CDP datasets are not practically usable to evaluate the performance of authentication algorithms. In short, the apparently simple question: “are copy detection patterns secure against copy?”, remains unanswered as of today. The primary contribution of this paper is to present a publicly available dataset of CDPs including multiple types of copies and attacks, allowing to systematically compare the performance level of CDPs against different attacks proposed in the prior art. The specific case in which a CDP is the same for an entire batch of prints, which is of practical importance as it covers applications with widely used industrial printers such as offset, flexo and rotogravure, is also studied. A second contribution is to highlight the role played by the CDP detector and its different processing steps. Indeed, depending on the specific processing involved, the detection performance can widely outperform the CDP bit error rate which has been used as a reference metrics in the prior art.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123190500","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648383
Toshiki Itagaki, Yuki Funabiki, T. Akishita
In this paper, we propose a robust image hashing method that enables detecting small tampering. Existing hashing methods are too robust, and the trade-off relation between the robustness and the sensitivity to visual content changes needs to be improved to detect small tampering. Though the adaptive thresholding method can improve the trade-off, there's more room to improve and it requires tampered image derived from the original, which limits its applications. To overcome these two drawbacks, we introduce a new concept of a hyperrectangular region in multi-dimensional hash space, which is determined at the timing of hash generation as the region that covers a hash cluster by using the maximum and the minimum of the cluster per each hash axis. We evaluate our method and the existing methods. Our method improves the trade-off, which achieves 0.9428 as AUC (Area Under the Curve) for detecting tampering that occupies about 0.1% area of the image in the presence of JPEG compression and reducing the size as content-preserving operations. Furthermore, our method does not require tampered image derived from the original, which differs from the existing method.
{"title":"Robust Image Hashing for Detecting Small Tampering Using a Hyperrectangular Region","authors":"Toshiki Itagaki, Yuki Funabiki, T. Akishita","doi":"10.1109/WIFS53200.2021.9648383","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648383","url":null,"abstract":"In this paper, we propose a robust image hashing method that enables detecting small tampering. Existing hashing methods are too robust, and the trade-off relation between the robustness and the sensitivity to visual content changes needs to be improved to detect small tampering. Though the adaptive thresholding method can improve the trade-off, there's more room to improve and it requires tampered image derived from the original, which limits its applications. To overcome these two drawbacks, we introduce a new concept of a hyperrectangular region in multi-dimensional hash space, which is determined at the timing of hash generation as the region that covers a hash cluster by using the maximum and the minimum of the cluster per each hash axis. We evaluate our method and the existing methods. Our method improves the trade-off, which achieves 0.9428 as AUC (Area Under the Curve) for detecting tampering that occupies about 0.1% area of the image in the presence of JPEG compression and reducing the size as content-preserving operations. Furthermore, our method does not require tampered image derived from the original, which differs from the existing method.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133600082","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648393
Bhavin Jawade, Akshay Agarwal, S. Setlur, N. Ratha
Traditional fingerprint authentication requires the acquisition of data through touch-based specialized sensors. However, due to many hygienic concerns including the global spread of the COVID virus through contact with a surface has led to an increased interest in contactless fingerprint image acquisition methods. Matching fingerprints acquired using contactless imaging against contact-based images brings up the problem of performing cross modal fingerprint matching for identity verification. In this paper, we propose a cost-effective, highly accurate and secure end-to-end contactless fingerprint recognition solution. The proposed framework first segments the finger region from an image scan of the hand using a mobile phone camera. For this purpose, we developed a cross-platform mobile application for fingerprint enrollment, verification, and authentication keeping security, robustness, and accessibility in mind. The segmented finger images go through fingerprint enhancement to highlight discriminative ridge-based features. A novel deep convolutional network is proposed to learn a representation from the enhanced images based on the optimization of various losses. The proposed algorithms for each stage are evaluated on multiple publicly available contactless databases. Our matching accuracy and the associated security employed in the system establishes the strength of the proposed solution framework.
{"title":"Multi Loss Fusion For Matching Smartphone Captured Contactless Finger Images","authors":"Bhavin Jawade, Akshay Agarwal, S. Setlur, N. Ratha","doi":"10.1109/WIFS53200.2021.9648393","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648393","url":null,"abstract":"Traditional fingerprint authentication requires the acquisition of data through touch-based specialized sensors. However, due to many hygienic concerns including the global spread of the COVID virus through contact with a surface has led to an increased interest in contactless fingerprint image acquisition methods. Matching fingerprints acquired using contactless imaging against contact-based images brings up the problem of performing cross modal fingerprint matching for identity verification. In this paper, we propose a cost-effective, highly accurate and secure end-to-end contactless fingerprint recognition solution. The proposed framework first segments the finger region from an image scan of the hand using a mobile phone camera. For this purpose, we developed a cross-platform mobile application for fingerprint enrollment, verification, and authentication keeping security, robustness, and accessibility in mind. The segmented finger images go through fingerprint enhancement to highlight discriminative ridge-based features. A novel deep convolutional network is proposed to learn a representation from the enhanced images based on the optimization of various losses. The proposed algorithms for each stage are evaluated on multiple publicly available contactless databases. Our matching accuracy and the associated security employed in the system establishes the strength of the proposed solution framework.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128146085","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648400
Yassine Yousfi, Jan Butora, J. Fridrich
While convolutional neural networks have firmly established themselves as the superior steganography detectors, little human-interpretable feedback to the steganographer as to how the network reaches its decision has so far been obtained from trained models. The folklore has it that, unlike rich models, which rely on global statistics, CNNs can leverage spatially localized signals. In this paper, we adapt existing attribution tools, such as Integrated Gradients and Last Activation Maps, to show that CNNs can indeed find overwhelming evidence for steganography from a few highly localized embedding artifacts. We look at the nature of these artifacts via case studies of both modern content-adaptive and older steganographic algorithms. The main culprit is linked to “content creating changes” when the magnitude of a DCT coefficient is increased (Jsteg, –F5), which can be especially detectable for high frequency DCT modes that were originally zeros (J-MiPOD). In contrast, J-UNIWARD introduces the smallest number of locally detectable embedding artifacts among all tested algorithms. Moreover, we find examples of inhibition that facilitate distinguishing between the selection channels of stego algorithms in a multi-class detector. The authors believe that identifying and characterizing local embedding artifacts provides useful feedback for future design of steganographic schemes.
{"title":"CNN Steganalyzers Leverage Local Embedding Artifacts","authors":"Yassine Yousfi, Jan Butora, J. Fridrich","doi":"10.1109/WIFS53200.2021.9648400","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648400","url":null,"abstract":"While convolutional neural networks have firmly established themselves as the superior steganography detectors, little human-interpretable feedback to the steganographer as to how the network reaches its decision has so far been obtained from trained models. The folklore has it that, unlike rich models, which rely on global statistics, CNNs can leverage spatially localized signals. In this paper, we adapt existing attribution tools, such as Integrated Gradients and Last Activation Maps, to show that CNNs can indeed find overwhelming evidence for steganography from a few highly localized embedding artifacts. We look at the nature of these artifacts via case studies of both modern content-adaptive and older steganographic algorithms. The main culprit is linked to “content creating changes” when the magnitude of a DCT coefficient is increased (Jsteg, –F5), which can be especially detectable for high frequency DCT modes that were originally zeros (J-MiPOD). In contrast, J-UNIWARD introduces the smallest number of locally detectable embedding artifacts among all tested algorithms. Moreover, we find examples of inhibition that facilitate distinguishing between the selection channels of stego algorithms in a multi-class detector. The authors believe that identifying and characterizing local embedding artifacts provides useful feedback for future design of steganographic schemes.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114869290","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648395
Shashank Arora, P. Atrey
With the advent of cloud-based collaborative editing, there have been security and privacy concerns about user data since the users are not the sole owners of the data stored over the cloud. Most secure collaborative editing solutions thus employ the use of AES to secure user content. In this work, we explore the use of secret sharing to maintain the confidentiality of user data in a collaborative document. We establish that using secret sharing provides an average increase of 56.01% in performance over AES with a single set of coefficients and an average performance increase of 30.37% with multiple sets of coefficients, while not requiring maintenance and distribution of symmetric keys as in the case of AES. We discuss the incorporation of keyword-based search with the proposed framework and present the operability and security analysis.
{"title":"Secure Collaborative Editing Using Secret Sharing","authors":"Shashank Arora, P. Atrey","doi":"10.1109/WIFS53200.2021.9648395","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648395","url":null,"abstract":"With the advent of cloud-based collaborative editing, there have been security and privacy concerns about user data since the users are not the sole owners of the data stored over the cloud. Most secure collaborative editing solutions thus employ the use of AES to secure user content. In this work, we explore the use of secret sharing to maintain the confidentiality of user data in a collaborative document. We establish that using secret sharing provides an average increase of 56.01% in performance over AES with a single set of coefficients and an average performance increase of 30.37% with multiple sets of coefficients, while not requiring maintenance and distribution of symmetric keys as in the case of AES. We discuss the incorporation of keyword-based search with the proposed framework and present the operability and security analysis.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"14 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120859748","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648394
Simon Kirchgasser, Christof Kauba, A. Uhl
The availability of biometric data (here fingerprint samples) is a crucial requirement in all areas of biometrics. Due to recent changes in cross-border regulations (GDPR) sharing and accessing biometric sample data has become more difficult. An alternative way to facilitate a sufficient amount of test data is to synthetically generate biometric samples, which has its limitations. One of them is the generated data being not realistic enough and a more common one is that most free solutions are not able to generate mated samples, especially for fingerprints. In this work we propose a multi-level methodology to assess synthetically generated fingerprint data in terms of their similarity to real fingerprint samples. Furthermore, we present a generic approach to extend an existing synthetic fingerprint generator to be able to produce mated samples on the basis of single instances of non-mated ones which is then evaluated using the aforementioned multi-level methodology.
{"title":"Assessment of Synthetically Generated Mated Samples from Single Fingerprint Samples Instances","authors":"Simon Kirchgasser, Christof Kauba, A. Uhl","doi":"10.1109/WIFS53200.2021.9648394","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648394","url":null,"abstract":"The availability of biometric data (here fingerprint samples) is a crucial requirement in all areas of biometrics. Due to recent changes in cross-border regulations (GDPR) sharing and accessing biometric sample data has become more difficult. An alternative way to facilitate a sufficient amount of test data is to synthetically generate biometric samples, which has its limitations. One of them is the generated data being not realistic enough and a more common one is that most free solutions are not able to generate mated samples, especially for fingerprints. In this work we propose a multi-level methodology to assess synthetically generated fingerprint data in terms of their similarity to real fingerprint samples. Furthermore, we present a generic approach to extend an existing synthetic fingerprint generator to be able to produce mated samples on the basis of single instances of non-mated ones which is then evaluated using the aforementioned multi-level methodology.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133944539","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 : 2021-12-07DOI: 10.1109/WIFS53200.2021.9648385
Michael Albright, Nitesh Menon, Kristy Roschke, Arslan Basharat
The rapid increase in the amount of online disinformation warrants new and robust digital forensics methods for validating purported sources of multimodal news articles. We conducted a survey of news photojournalists for insights into their workflows. A high percentage (91%) of respondents reported standardized photo publishing procedures, which we hypothesize facilitates source verification. In this work, we demonstrate that the online news sites leave predictable and discernible patterns in the compression settings of the images they publish. We propose novel, simple, and very efficient algorithms to analyze the image compression profiles for news source verification and identification. We evaluate the algorithms' effectiveness through extensive experiments on a newly-released dataset of over 64K images from over 34K articles collected from 30 news sites. The image compression features are modeled by Naive Bayes variants or XGBoost classifiers for source attribution and verification. For these news sources we are able to achieve very strong performance with the proposed algorithms resulting in 0.92–0.94 average AUC for source verification under a closed set scenario, and compelling open set generalization with only 0.0–0.04 reduction in the average AUC.
{"title":"Source Attribution of Online News Images by Compression Analysis","authors":"Michael Albright, Nitesh Menon, Kristy Roschke, Arslan Basharat","doi":"10.1109/WIFS53200.2021.9648385","DOIUrl":"https://doi.org/10.1109/WIFS53200.2021.9648385","url":null,"abstract":"The rapid increase in the amount of online disinformation warrants new and robust digital forensics methods for validating purported sources of multimodal news articles. We conducted a survey of news photojournalists for insights into their workflows. A high percentage (91%) of respondents reported standardized photo publishing procedures, which we hypothesize facilitates source verification. In this work, we demonstrate that the online news sites leave predictable and discernible patterns in the compression settings of the images they publish. We propose novel, simple, and very efficient algorithms to analyze the image compression profiles for news source verification and identification. We evaluate the algorithms' effectiveness through extensive experiments on a newly-released dataset of over 64K images from over 34K articles collected from 30 news sites. The image compression features are modeled by Naive Bayes variants or XGBoost classifiers for source attribution and verification. For these news sources we are able to achieve very strong performance with the proposed algorithms resulting in 0.92–0.94 average AUC for source verification under a closed set scenario, and compelling open set generalization with only 0.0–0.04 reduction in the average AUC.","PeriodicalId":196985,"journal":{"name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123750763","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：