Benchmarking Spatial, Spectral, and Self-Supervised Cues for Face Forgery Detection under Realistic Degradation

• URL: http://arxiv.org/abs/2609.01511v1
• Date: Tue, 01 Sep 2026 16:41:55 GMT
• Title: Benchmarking Spatial, Spectral, and Self-Supervised Cues for Face Forgery Detection under Realistic Degradation
• Abstract summary: This paper presents a standardized benchmark for face forgery detection using the Multi-Dimensional Face Forgery Image (MFFI) dataset.<n>We compare six model families, including convolutional networks, transformer-based models, and a frozen self-supervised DINOv3 backbone.<n>Xception with RGB obtains the best clean performance, reaching 0.884 mean ROC-AUC, but degrades substantially on the harder partition.
• Score: 0.6619186532078049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face forgery detectors often achieve strong results on controlled benchmarks, but their reliability under realistic image degradations remains limited. This paper presents a standardized benchmark for face forgery detection using the Multi-Dimensional Face Forgery Image (MFFI) dataset and evaluates performance on both clean and degraded test partitions. We compare six model families, including convolutional networks, transformer-based models, and a frozen self-supervised DINOv3 backbone, across spatial, spectral, and hybrid input representations. The results show that clean-set performance is not a reliable indicator of robustness under compression, resizing, and blurring. Xception with RGB obtains the best clean performance, reaching 0.884 mean ROC-AUC, but degrades substantially on the harder partition. In contrast, frozen DINOv3 achieves the strongest degraded-set result, with 0.726 mean ROC-AUC, while training only a linear classification head. The representation analysis indicates that Fourier-domain cues are most useful when combined with RGB information, whereas purely spectral inputs consistently underperform spatial representations. Qualitative attribution maps further suggest that convolutional detectors focus on localized artifacts, while DINOv3 relies on broader facial structure. These findings reinforce the need for degraded evaluation protocols and highlight self-supervised visual representations as a promising direction for robust face forgery detection. Our source code is publicly available at https://github.com/lucasdocunha/FaceForgery-Benchmark/.

Related papers

• Adaptive Gated Deepfake Detection for Low-Resolution and Resource-Constrained Environments [0.0]
  This paper proposes AdaGate-DF, an adaptive gated deepfake detection framework.<n>AdaGate-DF uses image-quality cues to route samples through a dual multi-exit system so high-quality images can exit earlier and save compute.
  arXiv  Detail & Related papers  (2026-09-04T16:14:57Z)
• AEGIS: A Semantic GAN and Evidential Learning Frameworkfor Robust Adversarial Detection in Vision Sensors [11.468095590140777]
  AEGIS is a semantic aware and uncertainty guided adversarial detection framework for vision sensors pipelines.<n>A SemantiGAN module functions as a multi class semantic discriminator, identifying and filtering visually inconsistent adversarial inputs.<n> Evaluations on the Tiny ImageNet dataset demonstrate the effectiveness of AEGIS.
  arXiv  Detail & Related papers  (2026-06-25T12:03:02Z)
• Employing Vision-Language Models for Face Image Quality Assessment [0.023141219541346198]
  Face Image Quality Assessment (FIQA) is a crucial control step in biometric pipelines.<n>State-of-the-art FIQA methods achieve high utility but typically operate as "black boxes"<n>We investigate the potential of off-the-shelf Vision-Language Models (VLMs) to bridge this gap by performing FIQA in a zero-shot setting.
  arXiv  Detail & Related papers  (2026-05-17T14:57:52Z)
• HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation [50.46910397782266]
  Hyperspectral image (HSI) restoration is crucial for reliable analysis, as real HSIs suffer from degradations like noise, blur, and resolution loss.<n>We present HIR-ALIGN, a plug-and-play target-adaptive augmentation framework that enhances hyperspectral image restoration by augmenting limited training images with synthetic data that closely matches the target distribution using no extra data.
  arXiv  Detail & Related papers  (2026-05-13T14:14:13Z)
• Attack-Aware Deepfake Detection under Counter-Forensic Manipulations [0.30586855806896035]
  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.
  arXiv  Detail & Related papers  (2025-12-26T04:05:52Z)
• ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack [56.0056378072843]
  We show that highly transferable adversarial images can be crafted solely via the upstream model.<n>We propose ForensicsSAM, a unified IFDL framework with built-in adversarial robustness.
  arXiv  Detail & Related papers  (2025-08-10T16:03:44Z)
• Practical Manipulation Model for Robust Deepfake Detection [55.2480439325792]
  We develop a more real-world degradation model in the area of image super-resolution.<n>We extend the space of pseudo-fakes by using Poisson blending, more diverse masks, generator artifacts, and distractors.<n>We show clear increases of $3.51%$ and $6.21%$ AUC on the DFDC and DFDCP datasets, respectively.
  arXiv  Detail & Related papers  (2025-06-05T15:06:16Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance [0.562479170374811]
  Per-IMage Overlap (PIMO) is a novel metric that addresses the shortcomings of AUROC and AUPRO. measuring recall per image simplifies computation and is more robust to noisy annotations. Our experiments demonstrate that PIMO offers practical advantages and nuanced performance insights.
  arXiv  Detail & Related papers  (2024-01-03T21:24:44Z)
• Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues [61.37306431455152]
  We propose a novel Hierarchical Forgery for Multi-modality Face Forgery Detection (HFC-MFFD) The HFC-MFFD learns robust patches-based hybrid representation to enhance forgery authentication in multiple-modality scenarios. The specific hierarchical face forgery is proposed to alleviate the class imbalance problem and further boost detection performance.
  arXiv  Detail & Related papers  (2022-12-30T10:54:29Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.