Not Just a Matter of Scale: Recovering Forensic Traces to Build a Forensic-Enhanced Vision Backbone

Foundation models expose useful but uneven forensic evidence. We study whether a compact DINOv3-L backbone can recover evidence absent from its CLS readout, using DINOv3-7B only as a training-time teacher. Rank–nullity and orthogonal projection characterize this bottleneck and show when student patch features can reduce reconstruction error. We propose RELIC, which uses frozen teacher-relative residuals to guide persistent collectors across student layers; a decoder combines the collected evidence with the projected CLS into one Restored Token. A training-only Local Evidence-Aware Director (LEAD) further guides forensic readout. Rather than matching teacher layers directly, RELIC fixes a residual coordinate system first and then trains collection and readout in stages; a controlled ablation shows that this separation avoids unstable joint transfer. At inference, the teacher and LEAD are removed, leaving a representation usable for standalone classification or plug-and-play detector replacement. On AIGIBench Setting II, RELIC reaches 85.3% domain-macro accuracy, compared with 77.6% for AIDE, 82.3% for DGS-Net, and 75.5% for RA-Det with its original DINOv3-L interface; it reaches 91.4% on Chameleon. Probes and corruption tests further show that the recovered residual carries usable complementary evidence.