Cross-Domain Generalization Limits of Vision Foundation Models in Facial Deepfake Detection

• URL: http://arxiv.org/abs/2605.24965v1
• Date: Sun, 24 May 2026 09:33:46 GMT
• Title: Cross-Domain Generalization Limits of Vision Foundation Models in Facial Deepfake Detection
• Abstract summary: generative models have enabled the creation of hyper-realistic facial deepfakes.<n>Traditional networks suffer from representation collapse, overfitting to localized artifact fingerprints.<n>This work investigates whether modern Vision Foundation Models can serve as generalizable, out-of-the-box feature extractors.
• Score: 2.538209532048867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid evolution of generative models has enabled the creation of hyper-realistic facial deepfakes, exposing a critical vulnerability in modern digital forensics: the inability of detectors to generalize to unseen manipulation techniques. Traditional networks suffer from representation collapse, overfitting to localized artifact fingerprints of specific training generators. This work investigates whether modern Vision Foundation Models can serve as generalizable, out-of-the-box feature extractors capable of tracking forensic anomalies across entirely unseen generative manifolds. We conduct a systematic cross-domain evaluation comparing three foundational learning paradigms: fully supervised macro-semantic features (RoPE-ViT), pure self-supervised geometric features (DINOv3), and multi-teacher agglomerative representations (NVIDIA C-RADIOv4-H). By deploying frozen backbones subjected to downstream linear probing, we map the performance limitations of these architectures on the challenging DF40 benchmark. Our empirical findings expose the intrinsic trade-offs between pre-training paradigms and parameter scale, proving that while foundation models retain high discriminative capabilities for entire face synthesis, localized face editing techniques expose fundamental boundaries in linear probe evaluation structures. Source code and model weights are available in http://github.com/mribrahim/deepfake

Related papers

• Deepfake Forensics Adapter: A Dual-Stream Network for Generalizable Deepfake Detection [22.889849855283355]
  Deepfake Forensics Adapter (DFA) is a novel dual-stream framework that synergizes vision-language foundation models with targeted forensics analysis.<n>Our approach integrates a pre-trained CLIP model with three core components to achieve specialized deepfake detection.<n>Our framework not only demonstrates state-of-the-art performance, but also points out a feasible and effective direction for developing a robust deepfake detection system.
  arXiv  Detail & Related papers  (2026-03-02T04:58:00Z)
• Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models [15.709482146201283]
  A simple linear classifier trained on the frozen features of modern Vision Foundation Models establishes a new state-of-the-art.<n>We show that this baseline matches specialized detectors on standard benchmarks but also decisively outperforms them on in-the-wild datasets.<n>We conclude by advocating for a paradigm shift in AI forensics, moving from overfitting on static benchmarks to harnessing the evolving world knowledge of foundation models for real-world reliability.
  arXiv  Detail & Related papers  (2026-02-02T07:20:02Z)
• Beyond Binary Classification: A Semi-supervised Approach to Generalized AI-generated Image Detection [1.189955933770711]
  A critical vulnerability in current forensics is the failure of detectors to achieve cross-generator generalization.<n>We propose a semi-supervised approach that enhances binary classification by discovering latent architectural patterns within the "fake" class.
  arXiv  Detail & Related papers  (2025-11-23T16:02:27Z)
• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes [9.462613446025001]
  Face-fake Deepfake videos pose growing risks to digital security, privacy, and media integrity.<n>FAME is a framework designed to capture subtle artifacts specific to different face-generative models.<n>Results show that FAME consistently outperforms existing methods in both accuracy and runtime.
  arXiv  Detail & Related papers  (2025-06-13T05:47:09Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection [86.97062579515833]
  We introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset, comprising samples generated by tft28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance, showcasing a remarkable tft11.6% improvement over existing methods.
  arXiv  Detail & Related papers  (2023-12-16T14:27:06Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition [80.35852245488043]
  We propose a CNN based architecture enhanced with multiple branches formed by radial basis function (RBF) units. RBF units capture local patterns shared by similar instances using an intermediate representation. We show it is the incorporation of local information what makes the proposed model competitive.
  arXiv  Detail & Related papers  (2021-09-07T21:05:56Z)
• DeepFake Detection by Analyzing Convolutional Traces [0.0]
  We focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method. The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying convolutional generative process. Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process.
  arXiv  Detail & Related papers  (2020-04-22T09:02:55Z)

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.