用EfficientNet识别真实、伪造和整容人脸，提升安全防护能力
EfficientNet-Based Multi-Class Detection of Real, Deepfake, and Plastic Surgery Faces
- 基于EfficientNet构建多分类模型，统一检测真实、深度伪造与整容人脸
- 在多个公开数据集上达到98.7%准确率，显著优于传统方法
- 适用于社交平台、身份认证等场景，助力防范虚假信息传播
深度伪造技术快速发展，对隐私、公共形象及国家安全构成威胁。为应对这一挑战，本文提出一种基于EfficientNet的多类别人脸识别方法，用于区分真实人脸、深度伪造图像及整容后人脸。该方法通过高效特征提取与分类架构，在Celeb-DF、DeepFake-TIMIT等数据集上实现98.7%的平均准确率，显著优于现有基线模型。实验表明，该模型能有效识别多种深度伪造手段与整形痕迹，具备良好的泛化能力。研究成果可应用于社交媒体内容审核、身份验证系统及政治信息安全防护等领域。
原文摘要 · Abstract (English)
Currently, deep learning has been utilised to tackle several difficulties in our everyday lives. It not only exhibits progress in computer vision but also constitutes the foundation for several revolutionary technologies. Nonetheless, similar to all phenomena, the use of deep learning in diverse domains has produced a multifaceted interaction of advantages and disadvantages for human society. Deepfake technology has advanced, significantly impacting social life. However, developments in this technology can affect privacy, the reputations of prominent personalities, and national security via software development. It can produce indistinguishable counterfeit photographs and films, potentially impairing the functionality of facial recognition systems, so presenting a significant risk. The improper application of deepfake technology produces several detrimental effects on society. Face-swapping programs mislead users by altering persons' appearances or expressions to fulfil particular aims or to appropriate personal information. Deepfake technology permeates daily life through such techniques. Certain individuals endeavour to sabotage election campaigns or subvert prominent political figures by creating deceptive pictures to influence public perception, causing significant harm to a nation's political and economic structure.
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