Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection

• URL: http://arxiv.org/abs/2605.24906v3
• Date: Wed, 27 May 2026 09:39:25 GMT
• Title: Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection
• Abstract summary: Detecting AI-generated images (AIGI) remains challenging because detectors often fail to generalize to unseen generators.<n>We propose PROBE, a framework that improves detector generalization by actively exploring challenging regions of the generative process.
• Score: 69.6123714120735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting AI-generated images (AIGI) remains challenging because detectors often fail to generalize to unseen generators. Although existing methods are trained on large datasets, their performance still degrades when generation settings change, indicating that data scale alone is insufficient and that limited coverage of generative variations during training is a key factor. Studies on generative model editing show that small changes in internal representations can produce diverse and meaningful image variations, many of which are not explored under standard sampling. Leveraging this insight, we propose PROBE (Probing Robustness via Boundary Exploration), a framework that improves detector generalization by actively exploring challenging regions of the generative process. Instead of treating the generator as a fixed data source, PROBE uses the detector as a critic to steer the generator through manifold-level modifications, producing realistic samples that are difficult to classify. These samples expose failure cases that are uncommon under standard data sampling strategies and are used to refine the detector. Experimental results across multiple benchmarks indicate that PROBE enhances generalization to unseen generators, resulting in more generalizable AIGI detection performance. Code and models are available at https://github.com/Amamiya-C/PROBE-AIGI-Detection

Related papers

• Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling [0.2523415604068923]
  Fun-TSG is a customizable time series generator designed to support high-quality evaluation of anomaly detection systems.<n>Fun-TSG supports the creation of diverse, interpretable, and reproducible benchmarking scenarios.
  arXiv  Detail & Related papers  (2026-04-14T08:38:33Z)
• Universal Transformation of One-Class Classifiers for Unsupervised Anomaly Detection [51.73001988341294]
  Anomaly detection is typically formulated as a one-class classification problem.<n>We present a dataset folding method that transforms an arbitrary one-class classifier-based anomaly detector into a fully unsupervised method.
  arXiv  Detail & Related papers  (2026-02-13T16:54:12Z)
• Feature-Aware Test Generation for Deep Learning Models [0.5368630420272898]
  We introduce Detect, a feature-aware test generation framework for vision-based deep learning (DL) models.<n>It generates inputs by perturbing disentangled semantic attributes within the latent space.<n>It identifies which features lead to behavior shifts and uses a vision-language model for semantic attribution.
  arXiv  Detail & Related papers  (2026-01-20T15:41:06Z)
• FakeRadar: Probing Forgery Outliers to Detect Unknown Deepfake Videos [56.782373316571444]
  We propose FakeRadar, a novel deepfake video detection framework.<n>FakeRadar addresses the challenges of cross-domain generalization in real-world scenarios.
  arXiv  Detail & Related papers  (2025-12-16T17:11:45Z)
• Scaling Up AI-Generated Image Detection via Generator-Aware Prototypes [15.99138549265524]
  Generator-Aware Prototype Learning (GAPL) is a framework that constrains representation with a structured learning paradigm.<n>GAPL achieves state-of-the-art performance, showing superior detection accuracy across a wide variety of GAN and diffusion-based generators.
  arXiv  Detail & Related papers  (2025-12-15T04:58:08Z)
• Rethinking Cross-Generator Image Forgery Detection through DINOv3 [62.80415066351157]
  Cross-generator detection has emerged as a new challenge forgenerative models.<n>We show that frozen visual foundation models, especially DINOv3, already exhibit strong cross-generator detection capability.<n>We introduce a training-free token-ranking strategy followed by a lightweight linear probe to select a small subset of authenticity-relevant tokens.
  arXiv  Detail & Related papers  (2025-11-27T14:01:50Z)
• Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection [11.907536189598577]
  Current AIGC detectors often achieve near-perfect accuracy on images produced by the same generator used for training but struggle to generalize to outputs from unseen generators.<n>We trace this failure in part to latent prior bias: detectors learn shortcuts tied to patterns stemming from the initial noise vector rather than learning robust generative artifacts.<n>We propose On-Manifold Adversarial Training (OMAT), which generates adversarial examples that remain on the generator's output manifold.
  arXiv  Detail & Related papers  (2025-06-01T07:20:45Z)
• 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)
• SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes [7.553507857251396]
  We propose a novel deepfake detector, called SeeABLE, that formalizes the detection problem as a (one-class) out-of-distribution detection task. SeeABLE pushes perturbed faces towards predefined prototypes using a novel regression-based bounded contrastive loss. We show that our model convincingly outperforms competing state-of-the-art detectors, while exhibiting highly encouraging generalization capabilities.
  arXiv  Detail & Related papers  (2022-11-21T09:38:30Z)
• FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations [12.027711542565315]
  We design a framework to generalize the deepfake detector for both the known and unseen GAN models. Our framework generates the frequency-level perturbation maps to make the generated images indistinguishable from the real images. For experiments, we design new test scenarios varying from the training settings in GAN models, color manipulations, and object categories.
  arXiv  Detail & Related papers  (2022-02-07T16:45:11Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

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.