Abstract: With the continuous advancement of artificial intelligence and big data technologies, the threshold for making fake videos has been significantly reduced. Therefore, identifying whether a video has been tampered with is crucial for ensuring the authenticity and credibility of the information. Current mainstream video forgery detection methods rely on convolutional neural networks, which exhibit limited capability in capturing temporal dependencies and lack comprehensive understanding of global temporal patterns. To address this issue, this paper proposes a learnable Ptuning based method for video object removal tamper detection and localization. Firstly, the prior knowledge of the pretrained model is fully mined by learnable Ptuning, and multiview features such as spatial, temporal and highfrequency are efficiently extracted. Secondly, a multiscale feature interaction module is proposed to accurately capture the tampering traces from finegrained to coarsegrained through multiscale convolution operation and twostep decomposition strategy. Furthermore, a multiview fusion attention module is designed to significantly enhance the information sharing and fusion ability among multiview features via the crossview interaction mechanism. Experimental results demonstrate that the proposed method outperforms existing detection methods in both the time domain and the spatial domain.

Key words: video tamper detection, object removal, learnable Ptuning, multiscale feature interaction, multiple view feature

摘要： 随着人工智能和大数据技术的不断发展，制作伪造视频的门槛显著降低.因此，鉴别视频是否被篡改有助于确保信息的真实性和可信度.当前主流视频篡改检测方法依赖卷积神经网络，对时序依赖性捕捉能力有限，缺乏全局时间模式理解.为此，提出了一种基于可学习Ptuning的视频目标移除篡改检测与定位方法.首先，通过可学习Ptuning充分挖掘预训练模型的先验知识，高效提取空域、时序及高频等多视图特征.其次，提出了一种多尺度特征交互模块，通过多尺度卷积运算和2步分解策略，精准捕捉从细粒度至粗粒度的篡改痕迹.此外，设计了一种多路融合注意模块，通过跨视图交互机制，显著增强多视图特征之间的信息共享与融合能力.实验结果表明，该方法在时域及空域定位上的性能均优于现有检测方法.

关键词: 视频篡改检测, 目标移除, 可学习Ptuning, 多尺度特征交互, 多视图特征