Discovering Transferable Forensic Features for CNN-generated Images Detection

Keshigeyan Chandrasegaran, Ngoc-Trung Tran, A. Binder, Ngai-Man Cheung
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引用次数: 14

Abstract

Visual counterfeits are increasingly causing an existential conundrum in mainstream media with rapid evolution in neural image synthesis methods. Though detection of such counterfeits has been a taxing problem in the image forensics community, a recent class of forensic detectors -- universal detectors -- are able to surprisingly spot counterfeit images regardless of generator architectures, loss functions, training datasets, and resolutions. This intriguing property suggests the possible existence of transferable forensic features (T-FF) in universal detectors. In this work, we conduct the first analytical study to discover and understand T-FF in universal detectors. Our contributions are 2-fold: 1) We propose a novel forensic feature relevance statistic (FF-RS) to quantify and discover T-FF in universal detectors and, 2) Our qualitative and quantitative investigations uncover an unexpected finding: color is a critical T-FF in universal detectors. Code and models are available at https://keshik6.github.io/transferable-forensic-features/
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发现可转移的取证特征为cnn生成的图像检测
随着神经图像合成方法的快速发展,视觉伪造越来越成为主流媒体存在的难题。尽管在图像取证社区中,检测此类伪造图像一直是一个棘手的问题,但最近一类法医探测器——通用探测器——能够令人惊讶地发现伪造图像,而不考虑生成器架构、损失函数、训练数据集和分辨率。这一有趣的特性表明,在宇宙探测器中可能存在可转移的法医特征(T-FF)。在这项工作中,我们进行了第一次分析研究,以发现和理解宇宙探测器中的T-FF。我们的贡献有两个方面:1)我们提出了一种新的法医特征相关统计(FF-RS)来量化和发现通用探测器中的T-FF; 2)我们的定性和定量研究揭示了一个意想不到的发现:颜色是通用探测器中关键的T-FF。代码和模型可在https://keshik6.github.io/transferable-forensic-features/上获得
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