The paper argues that AI‑generated image detection has focused too much on model design and not enough on how supervision is structured. By replacing blended supervision with orthogonal, disentangled supervision—semantic signals for a vision‑language model and artifact signals for a vision expert—the authors show a 9.5% improvement on five in‑the‑wild benchmarks. They also introduce the AIGI‑Now benchmark to evaluate detectors on eight modern generators with separate semantic and pixel‑level tests.
arXiv:2510. 05740v2 Announce Type: replace-cross Abstract: The rapid development of generative models has made it increasingly crucial to develop detectors that can reliably detect synthetic images.
The paper investigates patch‑wise supervision for detecting AI‑generated images, proposing a shared backbone that classifies explicit crops with individual losses and averages patch probabilities only during inference. This approach eliminates the need for handcrafted residual filtering or learned image‑level fusion modules. Experiments across single‑patch selection, multiple generator collections, and four CNN and Transformer backbones show that patch‑wise variants outperform whole‑image counterparts on the GenImage dataset, while also exploring factors such as supervision granularity, source resolution, crop size, and inference coverage.
The paper introduces Relative Patch Response Learning (PRL) to detect AI‑generated images by comparing patch responses across two mixed views of aligned real‑generated image pairs. PRL uses a relative response objective, a reference coherence objective, and an area ranking objective to address contextual shifts caused by self‑attention, providing precise supervision for patch‑level detection. Experiments show PRL outperforms prior methods by 4.3% and 5.9% in average balanced accuracy on standard and in‑the‑wild benchmarks.
arXiv:2605.31192v2 Announce Type: replace Abstract: Generalizable deepfake detection requires complementary forensic and semantic visual evidence. Specialist encoders capture subtle manipulation trac...
The paper investigates whether pretrained language models can act as lightweight auxiliary supervision for supervised vision training, without requiring paired image-text data or complex objectives. Two forms of language guidance—Explicit Language Guidance (ExLG) and Implicit Language Guidance (ImLG)—are evaluated across six generalization regimes, including in‑distribution, out‑of‑distribution, shortcut resistance, texture/shape bias, adversarial robustness, and continual learning. Results show that ExLG improves in‑distribution, low‑data performance, and continual learning retention, while ImLG is more effective in shortcut‑sensitive and adversarial settings, and both add minimal parameters and training overhead.
arXiv:2512.17730v2 Announce Type: replace Abstract: Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specif...
arXiv:2505.07675v3 Announce Type: replace-cross Abstract: Semi-supervised learning (SSL) has emerged as a practical solution for addressing data scarcity challenges by leveraging unlabeled data. Rece...
arXiv:2608. 00716v1 Announce Type: cross Abstract: Robust detection of generated images is critical to counter the misuse of generative models.
arXiv:2604.09531v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition, largely be...
arXiv:2607. 04607v1 Announce Type: cross Abstract: The rapid advancement of AI-generated videos poses increasing security risks and calls for robust detectors with strong cross-domain generalization.
arXiv:2607. 18958v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks, posing significant security risks.
arXiv:2606. 08864v1 Announce Type: cross Abstract: The rapid adoption of diffusion and large-scale generative models has made it increasingly challenging to distinguish synthetic imagery from real photographs.