Detecting Forged HEVC Videos via Anomalous Bitrate-Compressed Traces: A Frame-Level Bitrate Analysis Framework

摘要

Forged videos are often subjected to double compression. When a forger maliciously or unintentionally increases the video's bitrate during re-encoding, the resulting videos are termed fake bitrate videos. Detecting these videos offers a generalized approach for efficiently identifying potentially forged content within large datasets. However, previous research has largely focused on video-level detection of fully fake bitrate videos, where an entire video is re-encoded at a higher bitrate after content modification or the creation of fake high-definition (HD) footage. In practice, a skilled forger may adjust the bitrate of only specific video segments, generating partial fake bitrate videos-a common manipulation in tampering processes like video splicing. Existing methods face difficulties in detecting such partial modifications at the frame level and in pinpointing the manipulated segments. Our study addresses this gap by introducing a novel frame-level detection approach, which significantly enhances forensic precision. We simultaneously account for two types of abnormal frames arising from re-encoding and bitrate escalation and, for the first time, define fake bitrate video detection as a triple classification problem. To meet the challenges of this task, we extract anomalous bitrate-compression traces that capture subtle differences among the three frame types. Additionally, we propose the Trident Transformer Network (TTNet), a model designed to effectively integrate and learn high-frequency information within the encoding domain. Our approach achieves substantial improvements in accuracy, surpassing state-of-the-art methods by 3.62% and 11.95% in video-level and frame-level detection scenarios, respectively.