通过频域特征提升视频修复痕迹检测准确率
Detecting Inpainted Video with Frequency Domain Insights
- 融合频域信息，识别不同修复算法的独特频率特征
- 在公开数据集上达到当前最佳性能，显著提升检测精度
- 适合内容安全、媒体真实性验证等场景使用
视频修复技术可实现帧内内容的无缝移除与替换，但若被滥用将带来伦理和法律风险。为降低此类风险，检测修复区域至关重要。以往方法多聚焦空间与时间维度特征，忽略了不同修复算法在频域中的独特表现。本文提出频域洞察网络（FDIN），通过自适应带通选择响应模块捕捉各类修复技术的频域特性，并引入基于快速傅里叶卷积的注意力模块识别修复区域中的周期性伪影。结合3D残差块进行时空分析，FDIN逐步从粗略判断细化至精确定位。在多个公开数据集上的实验表明，该方法达到当前最优性能，树立了视频修复检测的新基准。
原文摘要 · Abstract (English)
Video inpainting enables seamless content removal and replacement within frames, posing ethical and legal risks when misused. To mitigate these risks, detecting manipulated regions in inpainted videos is critical. Previous detection methods often focus solely on the characteristics derived from spatial and temporal dimensions, which limits their effectiveness by overlooking the unique frequency characteristics of different inpainting algorithms. In this paper, we propose the Frequency Domain Insights Network (FDIN), which significantly enhances detection accuracy by incorporating insights from the frequency domain. Our network features an Adaptive Band Selective Response module to discern frequency characteristics specific to various inpainting techniques and a Fast Fourier Convolution-based Attention module for identifying periodic artifacts in inpainted regions. Utilizing 3D ResBlocks for spatiotemporal analysis, FDIN progressively refines detection precision from broad assessments to detailed localization. Experimental evaluations on public datasets demonstrate that FDIN achieves state-of-the-art performance, setting a new benchmark in video inpainting detection.
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