Uncovering the impact of SNS processing on device source authentication: A comprehensive optimization approach

Abstract

In zero_shot device source authentication, Social Network Service (SNS) processing induces severe feature homogenization, masking device-specific fingerprints and triggering a “false confidence” paradox. This is a difficult problem, and despite active studies in recent years, it remains a great challenge. We propose a device-aware forensics framework, which integrates multimodal feature fusion, dual-verification, and a three-component optimization suite: test-time style normalization, mild transfer learning, and advanced confidence calibration. Experiments show our method elevates the camera device detection rate from a baseline of 17% to 94.0% (mean, validated over 10 independent runs), with an average confidence of 0.825 and an Expected Calibration Error (ECE) of 0.197. We reveal a trade-off between detection rate and calibration reliability, validating a “performance first, then calibration repair” optimization path. This work offers insights for building test-time adaptive and high-reliability forensic systems.