The detection of malicious Deepfakes is a constantly evolving problem, that requires continuous monitoring of detectors, to ensure they are able to detect image manipulations generated by the latest emerging models. In this paper, we present a preliminary study that investigates the vulnerability of single-image Deepfake detectors to attacks created by a representative of the newest generation of generative methods, i.e. Denoising Diffusion Models (DDMs). Our experiments are run on FaceForensics++, a commonly used benchmark dataset, consisting of Deepfakes generated with various techniques for face swapping and face reenactment. The analysis shows, that reconstructing existing Deepfakes with only one denoising diffusion step significantly decreases the accuracy of all tested detectors, without introducing visually perceptible image changes.

翻译：恶意深度伪造的检测是一个持续演化的问题，要求对检测器进行持续监测，以确保其能够检测由最新涌现模型生成的图像篡改。本文通过初步研究，探讨了单图像深度伪造检测器在面对新一代生成方法（即去噪扩散模型）所创建攻击时的脆弱性。实验基于常用基准数据集FaceForensics++开展，该数据集包含采用多种换脸及面部重现技术生成的深度伪造样本。分析表明，仅通过单步去噪扩散处理即可显著降低所有受测检测器的准确率，且不会对图像引入肉眼可感知的变化。