Detecting Double Compression in HEVC Videos via Neural Attention and Bitstream Analysis

Double compression detection plays a critical role in video forensics, as re-encoding operations often accompany video tampering, transcoding, or malicious content manipulation. Although double compression detection has been extensively studied for images and early video codecs, modern video standards such as High Efficiency Video Coding (HEVC) introduce new challenges due to flexible block partitioning, hierarchical prediction structures, and the widespread use of bidirectional frames. Existing forensic approaches either rely solely on codec-domain statistics or treat deep neural networks as black-box detectors, limiting both robustness and interpretability. In this paper, we propose a neural-guided bitstream forensic framework for detecting double compression in HEVC videos containing B-frames. The key idea is to exploit temporal instability in neural attention as an indicator of forced re-encoding decisions, and to link this instability to codec-level prediction anomalies arising from Group-of-Pictures (GOP) misalignment. Specifically, we extract HEVC bitstream features describing transform unit statistics and prediction behavior, and combine them with neural attention dynamics derived from frame-level attention maps. To quantify attention dynamics, we introduce Class Activation Map (CAM) change descriptors based on rank-biased overlap (RBO), capturing periodic disruptions in attention caused by relocated I-frames. The resulting hybrid representation is modeled using a bidirectional LSTM to capture temporal periodicity across the video sequence. Experimental results on HEVC datasets demonstrate that the proposed approach significantly improves double compression detection accuracy.