基于扩散模型的伪造人脸检测分析

Detection and Analysis of Synthetic Faces Based on Diffusion Models

深度学习图像生成技术已取得显著进展,其中扩散模型是一种高效的生成模型,它被广泛用于图像生成。然而,由扩散模型生成的图像存在潜在的隐私和数据安全风险,尤其是伪造人脸图像可能被恶意用于伪造身份和欺骗人脸识别系统。通过对主流伪造人脸图像检测器的评估,揭示了扩散模型与生成对抗网络在频域上存在差异的特征,验证了基于频域分析的有效检测方法,为保护隐私和数据安全提供了强有力的支持。

Significant progress has been made in deep learning image generation techniques,among which diffusion models stand out as efficient generative models widely used for image synthesis.However,images generated by diffusion models pose potential privacy and data security risks,especially synthetic facial images that could be maliciously employed for identity fraud and to deceive facial recognition systems.By evaluating leading synthetic face detectors,this paper reveals the characteristics of the differences in the frequency domain between diffusion models and generative adversarial networks,verifying the effectiveness of a detection method based on frequency domain analysis,thereby providing strong support for safeguarding privacy and data security.