Deepfake 取证分析：合成媒体篡改的源数据集归因与法律影响
Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation
AI总结：
本研究提出一种基于多特征分析的GAN生成图像源数据集归因取证框架，实现98%-99%的分类准确率，并探讨其在版权、隐私合规等法律场景的应用价值。
摘要：
由生成对抗网络（GANs，Generative Adversarial Networks）生成的合成媒体在真实性验证和数据集来源追踪方面带来了严峻挑战，引发了版权执法、隐私保护与合规层面的关键关切。本文提出了一种新型取证框架，通过可解释特征分析识别GAN生成图像的训练数据集（如CelebA或FFHQ）。该框架整合了谱变换（傅里叶/离散余弦变换DCT）、颜色分布度量和局部特征描述符（SIFT，尺度不变特征变换），提取嵌入在合成输出中的判别性统计特征。监督分类器（随机森林Random Forest、支持向量机SVM、XGBoost）在二分类（真实vs合成）和跨多种GAN架构（StyleGAN、AttGAN、GDWCT、StarGAN、StyleGAN2）的多类数据集归因任务中，准确率达到98%-99%。实验结果表明，频域特征（DCT/快速傅里叶变换FFT）在捕捉数据集特定伪影（如上采样模式和频谱不规则性）方面占主导地位，而颜色直方图则揭示了GAN训练中的隐式正则化策略。本文进一步探讨了法律与伦理影响，说明数据集归因如何在GDPR、加州AB 602等框架下应对版权侵权、个人数据未授权使用和监管合规问题。该框架推动了生成式建模领域的问责与治理，可应用于数字取证、内容审核和知识产权诉讼。
英文摘要：
Synthetic media generated by Generative Adversarial Networks (GANs) pose significant challenges in verifying authenticity and tracing dataset origins, raising critical concerns in copyright enforcement, privacy protection, and legal compliance. This paper introduces a novel forensic framework for identifying the training dataset (e.g., CelebA or FFHQ) of GAN-generated images through interpretable feature analysis. By integrating spectral transforms (Fourier/DCT), color distribution metrics, and local feature descriptors (SIFT), our pipeline extracts discriminative statistical signatures embedded in synthetic outputs. Supervised classifiers (Random Forest, SVM, XGBoost) achieve 98-99% accuracy in binary classification (real vs. synthetic) and multi-class dataset attribution across diverse GAN architectures (StyleGAN, AttGAN, GDWCT, StarGAN, and StyleGAN2). Experimental results highlight the dominance of frequency-domain features (DCT/FFT) in capturing dataset-specific artifacts, such as upsampling patterns and spectral irregularities, while color histograms reveal implicit regularization strategies in GAN training. We further examine legal and ethical implications, showing how dataset attribution can address copyright infringement, unauthorized use of personal data, and regulatory compliance under frameworks like GDPR and California's AB 602. Our framework advances accountability and governance in generative modeling, with applications in digital forensics, content moderation, and intellectual property litigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
