论文标题
发现CNN生成图像检测的可转移的法医特征
Discovering Transferable Forensic Features for CNN-generated Images Detection
论文作者
论文摘要
视觉假冒物越来越多地导致主流培养基中具有快速演化的神经图像合成方法的存在难题。尽管对这种伪造的发现一直是图像法医界的一个征税问题，但最近的一类法医探测器（通用探测器）都能够出人意料地发现伪造图像，无论发电机架构，损失功能，培训数据集和决议如何。这种有趣的特性表明，通用检测器中可能存在可转移的法医特征（T-FF）。在这项工作中，我们进行了第一个分析研究，以发现和理解通用检测器中的T-FF。我们的贡献是2倍：1）我们提出了一个新型的法医相关统计统计量（FF-RS），以量化和发现通用检测器中的T-FF，以及2）我们的定性和定量研究发现了一个意外的发现：颜色是通用检测器中的关键T-FF。代码和型号可在https://keshik6.github.io/transferable-forensic-features/
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/
