美颜滤镜会削弱深度伪造检测能力，影响安防系统可靠性。
Deceptive Beauty: Evaluating the Impact of Beauty Filters on Deepfake and Morphing Attack Detection
- 测试多种美颜滤镜对检测模型的影响
- 多款主流检测器性能显著下降
- 提醒需构建抗美化干扰的鲁棒模型
社交媒体中的美颜滤镜日益普及，引发对人脸图像与视频可信度的担忧，尤其影响深度伪造与变形攻击检测系统的有效性。本文系统评估了美颜滤镜对多个先进检测器在基准数据集上的影响。通过在多种平滑滤镜处理前后对比检测性能，发现多数检测器准确率明显下降，暴露了面部美化带来的新漏洞。研究强调需开发对视觉增强具有鲁棒性的检测模型，以应对现实场景中普遍存在的图像美化现象。
原文摘要 · Abstract (English)
Digital beautification through social media filters has become increasingly popular, raising concerns about the reliability of facial images and videos and the effectiveness of automated face analysis. This issue is particularly critical for digital manipulation detectors, systems aiming at distinguishing between genuine and manipulated data, especially in cases involving deepfakes and morphing attacks designed to deceive humans and automated facial recognition. This study examines whether beauty filters impact the performance of deepfake and morphing attack detectors. We perform a comprehensive analysis, evaluating multiple state-of-the-art detectors on benchmark datasets before and after applying various smoothing filters. Our findings reveal performance degradation, highlighting vulnerabilities introduced by facial enhancements and underscoring the need for robust detection models resilient to such alterations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。