Anti-Forensic Attacks Using Generative Adversarial Networks

Zhao, Xinwei

doi:10.1007/978-981-16-7621-5_17

Cited by 4 publications

(4 citation statements)

References 63 publications

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“…Recently, generative adversarial networks (GANs) have been successfully applied in several counter forensic tasks ( Stamm & Zhao, 2022 ). In Chen, Zhao & Stamm (2019) , a GAN is employed to perform camera model attacks on a specific camera model.…”

Section: Introductionmentioning

confidence: 99%

Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

Elliethy

2023

PeerJ Computer Science

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A noiseprint is a camera-related artifact that can be extracted from an image to serve as a powerful tool for several forensic tasks. The noiseprint is built with a deep learning data-driven approach that is trained to produce unique noise residuals with clear traces of camera-related artifacts. This data-driven approach results in a complex relationship that governs the noiseprint with the input image, making it challenging to attack. This article proposes a novel neural noiseprint transfer framework for noiseprint-based counter forensics. Given an authentic image and a forged image, the proposed framework synthesizes a newly generated image that is visually imperceptible to the forged image, but its noiseprint is very close to the noiseprint of the authentic one, to make it appear as if it is authentic and thus renders the noiseprint-based forensics ineffective. Based on deep content and noiseprint representations of the forged and authentic images, we implement the proposed framework in two different approaches. The first is an optimization-based approach that synthesizes the generated image by minimizing the difference between its content representation with the content representation of the forged image while, at the same time, minimizing the noiseprint representation difference from the authentic one. The second approach is a noiseprint injection-based approach, which first trains a novel neural noiseprint-injector network that can inject the noiseprint of an image into another one. Then, the trained noiseprint-injector is used to inject the noiseprint from the authentic image into the forged one to produce the generated image. The proposed approaches are generic and do not require training for specific images or camera models. Both approaches are evaluated on several datasets against two common forensic tasks: the forgery localization and camera source identification tasks. In the two tasks, the proposed approaches are able to significantly reduce several forensic accuracy scores compared with two noiseprint-based forensics methods while at the same time producing high-fidelity images. On the DSO-1 dataset, the reduction in the forensic accuracy scores has an average of 75%, while the produced images have an average PSNR of 31.5 dB and SSIM of 0.9. The source code of the proposed approaches is available on GitHub (https://github.com/ahmed-elliethy/nnt).

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Section: Introductionmentioning

confidence: 99%

Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

Elliethy

2023

PeerJ Computer Science

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“…A. Qureshi et al [21] provide an overview of various anti-forensic techniques and countermeasures proposed in the literature, together with a bibliographic analysis of vanguard publications in different areas. J. Yu et al [22,23] add a method for the general detection of these techniques using convolutional neural networks (CNNs).…”

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confidence: 99%

“…Several studies [22,23,31,32] use neural networks (CNNs and GANs) to detect or generate forensic images, demonstrating their effectiveness in learning forensic traces and generating sophisticated anti-forensic attacks. Other approaches [25,26] are based on the analysis of first-and second-order statistics and Laplacian modeling to detect contrast enhancement and anti-forensic techniques.…”

mentioning

confidence: 99%

“…Other approaches [25,26] are based on the analysis of first-and second-order statistics and Laplacian modeling to detect contrast enhancement and anti-forensic techniques. Methods based on CNN [22,31,32] and GAN (generative adversarial networks) [23,29] show high accuracy and robustness in detecting forgery and anti-forensic attacks. Anti-forensic approaches [25,29] focus on minimizing distortion and maintaining image quality, while others [26,27] identify anomalies introduced by anti-forensic techniques.…”

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confidence: 99%

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Systematic Review: Anti-Forensic Computer Techniques

González Arias,

Bermejo Higuera,

Rainer Granados

et al. 2024

Applied Sciences

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The main purpose of anti-forensic computer techniques, in the broadest sense, is to hinder the investigation of a computer attack by eliminating traces and preventing the collection of data contained in a computer system. Nowadays, cyber-attacks are becoming more and more frequent and sophisticated, so it is necessary to understand the techniques used by hackers to be able to carry out a correct forensic analysis leading to the identification of the perpetrators. Despite its importance, this is a poorly represented area in the scientific literature. The disparity of the existing works, together with the small number of articles, makes it challenging to find one’s way around the vast world of computer forensics. This article presents a comprehensive review of the existing scientific literature on anti-forensic techniques, mainly DFIR (digital forensics incident response), organizing the studies according to their subject matter and orientation. It also presents key ideas that contribute to the understanding of this field of forensic science and details the shortcomings identified after reviewing the state of the art.

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Good or Evil: Generative Adversarial Networks in Digital Forensics

Veksler,

Akkaya

2023

Advances in Information Security

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Anti-Forensic Attacks Using Generative Adversarial Networks

Cited by 4 publications

References 63 publications

Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

Systematic Review: Anti-Forensic Computer Techniques

Good or Evil: Generative Adversarial Networks in Digital Forensics

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