arXiv AI
Do Deepfake Detectors Work in Reality?
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Spectra: Multi-Scale Implicit Decomposition for Deepfake Detection
Spectra is a deepfake detection framework that uses implicit neural representations (INRs) to analyze how an image is reconstructed, producing multi-level reconstruction heatmaps. These heatmaps are fused with semantic visual features via a bidirectional mechanism, enabling the model to detect forgery cues that are less tied to specific generation pipelines. Experiments on several deepfake benchmarks show that Spectra outperforms detectors relying only on RGB appearance or semantic representations, and it also extends to face presentation attack detection.
arXiv Machine Learning
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$\mu$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors
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From GAN-Based Models to GPT-5: A Taxonomy-Guided Study of Human Performance in Detecting Deepfake Face Images
The paper introduces the Deepfake Face Artifact Taxonomy (DFAT) and uses it to create both general-purpose and model-specific training materials for detecting deepfake face images. An online experiment with 524 participants tested detection accuracy across five generative models—three GANs, one diffusion model, and one multimodal large language model (GPT‑5)—showing that detection performance varies strongly by model and that model‑specific training yields greater gains than general training. A five‑month follow‑up with 342 participants revealed stable detection accuracy and no increase in perceived stress, suggesting the training’s durability.
arXiv AI
Sep 15
Lightweight Generalized DeepFake Face Detection with WAVIE: Wavelet Augmented Vision Intermediate Embeddings
arXiv:2609.14437v1 Announce Type: cross Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
arXiv Computer Vision
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Scaling Laws for Deepfake Detection
arXiv:2510.16320v2 Announce Type: replace Abstract: This paper presents a systematic study of scaling laws for the deepfake detection task. Specifically, we analyze the model performance against the...
arXiv Computer Vision
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Towards Generalizable Deepfake Detection via Real Distribution Bias Correction
arXiv:2603.14005v2 Announce Type: replace Abstract: To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using...