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Recapture is often used to hide the traces left by some operations such as JPEG compression, copy-move, etc. However, various
detectors have been proposed to detect recaptured images. To counter
these techniques, in this paper, we propose a method that can translate
recaptured images to fake \original images" to fool both human and machines. Our me...
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Citations
... • Untargeted attack: An adversarial sample is generated for an image such that the annotation on it is independent of the original annotation, i.e., as long as the attack succeeds, there is no restriction on which class the adversarial sample ultimately belongs to. Because of the vulnerability and instability inherent in deep neural networks, researchers have provided more research space in the field of adversarial attacks on deep neural networks by proposing various attack methods [30]- [33]. This paper presents several representative models of adversarial attacks. ...
Deep learning has become one of the most popular research topics today. Researchers have developed cutting-edge learning algorithms and frameworks around deep learning, applying them to a wide range of fields to solve real-world problems. However, we are more concerned about the security risks associated with deep learning models themselves—such as adversarial attacks, which will be discussed in this article. Attackers can use the deep learning model to create the conditions for an attack, maliciously manipulating the input images to deceive the classification model and produce false positives. This paper proposes a method of pre-denoising all input images to prevent adversarial attacks by adding a purification layer before the classification model. The method in this paper is proposed based on the basic architecture of Conditional Generative Adversarial Networks. It adds the image perception loss to the original algorithm Pix2pix to achieve more efficient image recovery. Our method can recover noise-attacked images to a level close to the actual image to ensure the correctness of the classification results. Experimental results show that our approach can quickly recover noisy images, and the recovery accuracy is 20.22% higher than the previous state-of-the-art.
... An anti-forensic method for recaptured image detection was proposed by Zhao et al. [176]. The authors proposed to employ Cycle-GANs typically used for image translation to accomplish this anti-forensic task of hiding traces of image recapturing. ...
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Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.
Due to the copyright issues often involved in the recapture of LCD screen content, recaptured screen image identification has received lots of concerns in image source forensics. This paper analyzes the characteristics of convolutional neural network (CNN) and vision transformer (ViT) in extracting features and proposes a cascaded network structure that combines local-feature and global-feature extraction modules to detect the recaptured screen image from original images with or without demoiréing operation. We first extract the local features of the input images with five convolutional layers and feed the local features into the ViT to enhance the local perception capability of the ViT module, and further extract the global features of the input images. Through thorough experiments, our method achieves a detection accuracy rate of 0.9691 in our generated dataset and 0.9940 in the existing mixture dataset, both showing the best performance among the compared methods.
