The study challenges the prevailing belief that frequency-based features and compression-invariant learning are essential for robust deepfake detection. Using a controlled, pre‑registered protocol, a simple EfficientNet‑B0 trained on diverse multi‑quality data outperformed the more complex CAFRL model across all compression levels, with a 3.66 AUC point advantage at CRF 40. After identifying and correcting four experimental defects, the authors found that frequency features added no marginal benefit, while data diversity—particularly real constant‑rate‑factor variants—proved to be the key factor for robustness against H.264 re‑encoding.
arXiv:2607. 06615v1 Announce Type: cross Abstract: Image forgery detection is a critical task in digital forensics, yet many deep-learning localization approaches are typically GPU-accelerated and computationally heavier than handcrafted screening methods.
arXiv:2607. 13234v1 Announce Type: cross Abstract: Deepfake detectors that achieve near-perfect scores on academic benchmarks collapse on real-world content: recent in-the-wild evaluations report AUC drops of 45-50% for state-of-the-art open-source models.
arXiv:2610.34545v1 Announce Type: new Abstract: Visual-token compression is usually evaluated by clean accuracy and efficiency, but these averages do not reveal failures that compression introduces u...
COVER is a new video watermarking method that targets codec compression as its primary design goal. It embeds the watermark payload in the latent space of a frozen generative video autoencoder and recovers it by re‑encoding the received video into the same latent space. Using a differentiable codec surrogate bank, COVER achieves high bit accuracy across multiple codecs while keeping marked videos visually close to the originals.
The paper introduces Motion‑Aware Deepfake (MAD) videos, a new class of deepfakes generated by pose‑guided diffusion models that can animate entire human figures. To study this threat, the authors build the first MAD‑specific benchmark with 1.5 million frames from 1,363 real and 30,122 synthetic videos, and analyze the subtle high‑frequency artifacts and spectral fingerprints that arise from limited input frames. Leveraging these insights, they propose MoDA, a defense framework that combines spatial semantics with steganalysis‑rich frequency features, achieving high detection and attribution accuracy across in‑distribution, cross‑dataset, and zero‑shot scenarios, and demonstrating robustness against adaptive attacks.
PairCLIP‑SWA is a single‑backbone deepfake detector that fine‑tunes an OpenCLIP ViT‑L/14 visual tower using a class‑balanced corpus created by CLIP‑similarity‑guided subsampling and then applies stochastic weight averaging over the fine‑tuning trajectory. Trained on the IEEE SP Cup 2025 (DF‑Wild‑Cup) benchmark, the weight‑averaged model achieves 97.04 % accuracy, 98.64 % AU‑ROC and a 4.07 % equal error rate—outperforming the best published three‑backbone ensemble. The study shows that averaging stabilizes validation performance, prevents misranking on disjoint test sets, and that CLIP pre‑training, rather than ensembling, is the key driver of accuracy.
arXiv:2605.31192v2 Announce Type: replace Abstract: Generalizable deepfake detection requires complementary forensic and semantic visual evidence. Specialist encoders capture subtle manipulation trac...
arXiv:2610.07729v1 Announce Type: new Abstract: Visual tokens are a major source of inference cost in vision-language models, yet simple image downsampling remains a surprisingly strong compression b...
arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.
arXiv:2607. 21776v1 Announce Type: new Abstract: Talking-face (TF) deepfake generation synthesizes photore- alistic facial video from a static source image and an au- dio signal, producing forgeries that current image-based detectors consistently fail to identify.
arXiv:2608. 15113v1 Announce Type: cross Abstract: Learned image compression (LIC) has demonstrated remarkable rate-distortion (RD) performance in benign settings.
arXiv:2606. 04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations.