Present and Future Generalization of Synthetic Image Detectors

• URL: http://arxiv.org/abs/2409.14128v2
• Date: Tue, 26 Nov 2024 09:12:30 GMT
• Title: Present and Future Generalization of Synthetic Image Detectors
• Authors: Pablo Bernabeu-Perez, Enrique Lopez-Cuena, Dario Garcia-Gasulla,
• Abstract summary: This work conducts a systematic analysis and uses its insights to develop practical guidelines for training robust synthetic image detectors. Model generalization capabilities are evaluated across different setups including real-world deployment conditions. We show that while current approaches excel in specific scenarios, no single detector achieves universal effectiveness.
• Score: 0.6144680854063939
• License:
• Abstract: The continued release of increasingly realistic image generation models creates a demand for synthetic image detectors. To build effective detectors we must first understand how factors like data source diversity, training methodologies and image alterations affect their generalization capabilities. This work conducts a systematic analysis and uses its insights to develop practical guidelines for training robust synthetic image detectors. Model generalization capabilities are evaluated across different setups (e.g. scale, sources, transformations) including real-world deployment conditions. Through an extensive benchmarking of state-of-the-art detectors across diverse and recent datasets, we show that while current approaches excel in specific scenarios, no single detector achieves universal effectiveness. Critical flaws are identified in detectors, and workarounds are proposed to enable the deployment of real-world detector applications enhancing accuracy, reliability and robustness beyond the limitations of current systems.

Related papers

• Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors [62.63467652611788]
  We introduce SEMI-TRUTHS, featuring 27,600 real images, 223,400 masks, and 1,472,700 AI-augmented images. Each augmented image is accompanied by metadata for standardized and targeted evaluation of detector robustness. Our findings suggest that state-of-the-art detectors exhibit varying sensitivities to the types and degrees of perturbations, data distributions, and augmentation methods used.
  arXiv  Detail & Related papers  (2024-11-12T01:17:27Z)
• Leveraging Mixture of Experts for Improved Speech Deepfake Detection [53.69740463004446]
  Speech deepfakes pose a significant threat to personal security and content authenticity. We introduce a novel approach for enhancing speech deepfake detection performance using a Mixture of Experts architecture.
  arXiv  Detail & Related papers  (2024-09-24T13:24:03Z)
• Zero-Shot Detection of AI-Generated Images [54.01282123570917]
  We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images. Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images. ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
  arXiv  Detail & Related papers  (2024-09-24T08:46:13Z)
• GM-DF: Generalized Multi-Scenario Deepfake Detection [49.072106087564144]
  Existing face forgery detection usually follows the paradigm of training models in a single domain. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets.
  arXiv  Detail & Related papers  (2024-06-28T17:42:08Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy [11.239248133240126]
  We seek a step toward a universal deepfake detection system with better generalization and robustness. We propose our Discrepancy Deepfake Detector framework, whose core idea is to learn the universal artifacts from multiple generators. Our framework achieves a 5.3% accuracy improvement in the OOD testing compared to the current SOTA methods while maintaining the ID performance.
  arXiv  Detail & Related papers  (2024-04-06T10:45:02Z)
• OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System [7.1083241462091165]
  We introduce an external modality-guided data mining framework, primarily rooted in optical character recognition (OCR), to extract statistical features from images. A key aspect of our approach is the alignment of external modality features, extracted using a single modality-aware model, with image features encoded by a convolutional neural network. Our methodology considerably boosts the recall rate of the defect detection model and maintains high robustness even in challenging scenarios.
  arXiv  Detail & Related papers  (2024-03-18T07:41:39Z)
• 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)
• Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation [27.483031588071942]
  GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes. We propose a robust detection framework based on a novel multi-view image completion representation. We evaluate the generalization ability of our framework across six popular GANs at different resolutions and its robustness against a broad range of perturbation attacks.
  arXiv  Detail & Related papers  (2023-06-02T08:38:02Z)
• 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)
• Fusing Global and Local Features for Generalized AI-Synthesized Image Detection [31.35052580048599]
  We design a two-branch model to combine global spatial information from the whole image and local informative features from patches selected by a novel patch selection module. We collect a highly diverse dataset synthesized by 19 models with various objects and resolutions to evaluate our model.
  arXiv  Detail & Related papers  (2022-03-26T01:55:37Z)

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.