Attack-Aware Deepfake Detection under Counter-Forensic Manipulations

• URL: http://arxiv.org/abs/2512.22303v1
• Date: Fri, 26 Dec 2025 04:05:52 GMT
• Title: Attack-Aware Deepfake Detection under Counter-Forensic Manipulations
• Authors: Noor Fatima, Hasan Faraz Khan, Muzammil Behzad,
• Abstract summary: This work presents an attack-aware deepfake and image-forensics detector designed for robustness, well-calibrated probabilities, and transparent evidence under realistic deployment conditions.<n>The method combines red-team training with randomized test-time defense in a two-stream architecture.<n>Results demonstrate near-perfect ranking across attacks, low calibration error, minimal abstention risk, and controlled tamper under regrain.
• Score: 0.30586855806896035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents an attack-aware deepfake and image-forensics detector designed for robustness, well-calibrated probabilities, and transparent evidence under realistic deployment conditions. The method combines red-team training with randomized test-time defense in a two-stream architecture, where one stream encodes semantic content using a pretrained backbone and the other extracts forensic residuals, fused via a lightweight residual adapter for classification, while a shallow Feature Pyramid Network style head produces tamper heatmaps under weak supervision. Red-team training applies worst-of-K counter-forensics per batch, including JPEG realign and recompress, resampling warps, denoise-to-regrain operations, seam smoothing, small color and gamma shifts, and social-app transcodes, while test-time defense injects low-cost jitters such as resize and crop phase changes, mild gamma variation, and JPEG phase shifts with aggregated predictions. Heatmaps are guided to concentrate within face regions using face-box masks without strict pixel-level annotations. Evaluation on existing benchmarks, including standard deepfake datasets and a surveillance-style split with low light and heavy compression, reports clean and attacked performance, AUC, worst-case accuracy, reliability, abstention quality, and weak-localization scores. Results demonstrate near-perfect ranking across attacks, low calibration error, minimal abstention risk, and controlled degradation under regrain, establishing a modular, data-efficient, and practically deployable baseline for attack-aware detection with calibrated probabilities and actionable heatmaps.

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