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arXiv 2407.06552cs.CRcs.CV

DLOVE:一种面向基于深度学习的水印技术的新型安全评估工具

DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques

Indian Institute of Technology Bhilai(印度比莱印度理工学院)

Sudev Kumar Padhi, Sk. Subidh Ali

AI总结:

该研究针对基于深度神经网络的水印技术开展安全分析,提出新型DLOVE覆写攻击方法,经7种主流水印技术验证其攻击有效性,可作为基准工具测试此类水印的鲁棒性。

摘要:

近年来,基于深度神经网络(DNN)的水印技术发展迅速,表现出卓越的性能。当前最先进的基于DNN的水印技术不仅在鲁棒性上超越了经典水印技术,还展现出对多种图像操作手段的抵抗能力。本文针对不同的基于DNN的水印技术开展了详尽的安全分析,提出了一类名为基于深度学习的覆写(Deep Learning-based OVErwriting, DLOVE)攻击的新型攻击方法,该方法利用对抗机器学习技术,可将含水印图像中的原始嵌入水印覆写为目标水印。据我们所知,这类攻击尚属首次提出。我们考虑了水印被用于设计构建对抗攻击的场景,分别在白盒和黑盒设置下开展研究。为验证该攻击的适配性与有效性,我们针对7种不同的水印技术——HiDDeN、ReDMark、PIMoG、Stegastamp、Aparecium、Distortion Agostic Deep Watermarking以及Hiding Images in an Image——实施了DLOVE攻击分析,这些技术均采用不同方法生成视觉上难以察觉的含水印图像。我们在多种约束条件下对这些水印技术开展的攻击分析,揭示了基于DNN的水印技术存在的安全漏洞,大量实验结果验证了DLOVE的攻击能力。我们提出将DLOVE作为基准安全分析工具,用于测试未来基于深度学习的水印技术的鲁棒性。

英文摘要:

Recent developments in Deep Neural Network (DNN) based watermarking techniques have shown remarkable performance. The state-of-the-art DNN-based techniques not only surpass the robustness of classical watermarking techniques but also show their robustness against many image manipulation techniques. In this paper, we performed a detailed security analysis of different DNN-based watermarking techniques. We propose a new class of attack called the Deep Learning-based OVErwriting (DLOVE) attack, which leverages adversarial machine learning and overwrites the original embedded watermark with a targeted watermark in a watermarked image. To the best of our knowledge, this attack is the first of its kind. We have considered scenarios where watermarks are used to devise and formulate an adversarial attack in white box and black box settings. To show adaptability and efficiency, we launch our DLOVE attack analysis on seven different watermarking techniques, HiDDeN, ReDMark, PIMoG, Stegastamp, Aparecium, Distortion Agostic Deep Watermarking and Hiding Images in an Image. All these techniques use different approaches to create imperceptible watermarked images. Our attack analysis on these watermarking techniques with various constraints highlights the vulnerabilities of DNN-based watermarking. Extensive experimental results validate the capabilities of DLOVE. We propose DLOVE as a benchmark security analysis tool to test the robustness of future deep learning-based watermarking techniques.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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