The paper introduces AdaGate-DF, an adaptive gated deepfake detection framework designed for low-resolution and resource-constrained environments. By leveraging image-quality cues, the system routes samples through a dual multi-exit architecture, allowing high-quality images to exit earlier and reduce computational load. Evaluations on Celeb-DF and FaceForensics++ show that AdaGate-DF outperforms existing models such as MaD-CoRN and DefakeHop++ while maintaining low inference latency and robust performance across varying resolutions and class imbalance scenarios.
arXiv:2608. 03096v1 Announce Type: cross Abstract: Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped.
Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped. In particular, the effectiveness of image-level detectors in the video domain has not been systematically assessed.
arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.
The paper examines how implementation details—such as data preprocessing, augmentation, and optimization—affect deepfake detector performance more than core architectural choices. By systematically isolating these factors across training, inference, and incremental updates, the authors identify design choices that consistently improve accuracy and generalization. These findings provide architecture‑agnostic best practices that enable state‑of‑the‑art performance on the AI‑GenBench benchmark.
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:2609.14437v1 Announce Type: cross Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
RT-DETRv4 introduces a cost‑effective distillation framework that leverages Vision Foundation Models to improve lightweight real‑time object detectors. The method uses a Deep Semantic Injector to embed high‑level VFM representations into deep detector layers and a Gradient‑guided Adaptive Modulation strategy to adjust semantic transfer intensity based on gradient norms. These techniques yield consistent performance gains across DETR‑based models without adding inference overhead, achieving state‑of‑the‑art COCO AP scores at high FPS.
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:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.
The study evaluates 16 AI‑generated image detection methods in a zero‑shot setting, testing 23 pretrained variants on 12 datasets with 2.6 million images from 291 generators. Results show no single detector consistently outperforms others, with accuracy ranging from 37.5 % to 75.0 %. The study highlights that training data alignment and generator type significantly affect performance, especially against modern commercial models, and identifies systematic failure patterns across datasets.
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:2502.10920v2 Announce Type: replace-cross Abstract: Deepfakes, particularly those involving faceswap-based manipulations, have sparked significant societal concern due to their increasing reali...