AI-generated video has revolutionized short video production, filmmaking, and
personalized media, making video local editing an essential tool. However, this
progress also blurs the line between reality and fiction, posing challenges in
multimedia forensics. To solve this urgent issue, V2A-Mark is proposed to
address the limitations of current video tampering forensics, such as poor
generalizability, singular function, and single modality focus. Combining the
fragility of video-into-video steganography with deep robust watermarking, our
method can embed invisible visual-audio localization watermarks and copyright
watermarks into the original video frames and audio, enabling precise
manipulation localization and copyright protection. We also design a temporal
alignment and fusion module and degradation prompt learning to enhance the
localization accuracy and decoding robustness. Meanwhile, we introduce a
sample-level audio localization method and a cross-modal copyright extraction
mechanism to couple the information of audio and video frames. The
effectiveness of V2A-Mark has been verified on a visual-audio tampering
dataset, emphasizing its superiority in localization precision and copyright
accuracy, crucial for the sustainable development of video editing in the AIGC
video era.

通过将不可见的视听定位水印和版权水印嵌入原始视频帧和音频中，实现精确的操作定位和版权保护，解决当前视频篡改法医学中的限制，包括泛化能力差、功能单一和单模态聚焦等问题。同时，我们还设计了一个时间对齐和融合模块以及退化提示学习来提高定位精度和解码鲁棒性，引入了示例级音频定位方法和跨模态版权提取机制以耦合音频和视频帧的信息，从而验证了 V2A-Mark 在视听篡改数据集上的有效性，并强调其在定位精度和版权准确性方面的优势，对 AIGC 视频时代的可持续发展至关重要。