一种基于胶囊网络的伪造图像检测方法设计

J. Manoharan
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引用次数: 0

摘要

由于最近媒体生成技术的改进,伪造最近在社会上变得更加普遍。在实时情况下,现代技术允许创建从社交网络获得的单个图像的伪造版本。伪造检测算法已经为各种领域创建;然而,随着新的攻击类型的出现,它们很快就会过时。提出了一种独特的基于深度学习算法的图像伪造检测策略。该方法采用卷积神经网络(CNN)从输入的RGB彩色图像中生成直方图表示,然后用于检测图像伪造。考虑到图像分离方法和复制-移动检测应用,本文提出的CNN结合了智能方法和直方图映射。在我们提出的工作的初始阶段,它用于检测假或真图像。此外,它还专门用于利用CNN模型进行图像层分离中的特征提取。为了同时捕获地理和直方图信息以及存在的可能性,我们在动态胶囊网络中使用向量从参考图像中检测伪造核。提出的研究工作有效地将智能与特征工程方法相结合。它们在识别伪造图像方面是众所周知的和有效的。计算性能指标,如准确性、召回率、精度和总错误率(HTER),并将其制成图表。
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Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Forgeries have recently become more prevalent in the society as a result of recent improvements in media generation technologies. In real-time, modern technology allows for the creation of a forged version of a single image obtained from a social network. Forgery detection algorithms have been created for a variety of areas; however they quickly become obsolete as new attack types exist. This paper presents a unique image forgery detection strategy based on deep learning algorithms. The proposed approach employs a convolutional neural network (CNN) to produce histogram representations from input RGB color images, which are then utilized to detect image forgeries. With the image separation method and copy-move detection applications in mind, the proposed CNN is combined with an intelligent approach and histogram mapping. It is used to detect fake or true images at the initial stage of our proposed work. Besides, it is specially designed for performing feature extraction in image layer separation with the help of CNN model. To capture both geographical and histogram information and the likelihood of presence at the same time, we use vectors in our dynamic capsule networks to detect the forgery kernels from reference images. The proposed research work integrates the intelligence with a feature engineering approach in an efficient manner. They are well-known and efficient in the identification of forged images. The performance metrics such as accuracy, recall, precision, and half total error rate (HTER) are computed and tabulated with the graph plot.
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