MLEP: Multi-granularity Local Entropy Patterns for Universal AI-generated Image Detection

• URL: http://arxiv.org/abs/2504.13726v1
• Date: Fri, 18 Apr 2025 14:50:23 GMT
• Title: MLEP: Multi-granularity Local Entropy Patterns for Universal AI-generated Image Detection
• Authors: Lin Yuan, Xiaowan Li, Yan Zhang, Jiawei Zhang, Hongbo Li, Xinbo Gao,
• Abstract summary: There is an urgent need for effective methods to detect AI-generated images (AIGI)<n>We propose Multi-granularity Local Entropy Patterns (MLEP), a set of entropy feature maps computed across shuffled small patches over multiple image scaled.<n>MLEP comprehensively captures pixel relationships across dimensions and scales while significantly disrupting image semantics, reducing potential content bias.
• Score: 44.40575446607237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advancements in image generation technologies have raised significant concerns about their potential misuse, such as producing misinformation and deepfakes. Therefore, there is an urgent need for effective methods to detect AI-generated images (AIGI). Despite progress in AIGI detection, achieving reliable performance across diverse generation models and scenes remains challenging due to the lack of source-invariant features and limited generalization capabilities in existing methods. In this work, we explore the potential of using image entropy as a cue for AIGI detection and propose Multi-granularity Local Entropy Patterns (MLEP), a set of entropy feature maps computed across shuffled small patches over multiple image scaled. MLEP comprehensively captures pixel relationships across dimensions and scales while significantly disrupting image semantics, reducing potential content bias. Leveraging MLEP, a robust CNN-based classifier for AIGI detection can be trained. Extensive experiments conducted in an open-world scenario, evaluating images synthesized by 32 distinct generative models, demonstrate significant improvements over state-of-the-art methods in both accuracy and generalization.

Related papers

• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Addressing Vulnerabilities in AI-Image Detection: Challenges and Proposed Solutions [0.0]
  This study evaluates the effectiveness of convolutional neural networks (CNNs) and DenseNet architectures for detecting AI-generated images.<n>We analyze the impact of updates and modifications such as Gaussian blurring, prompt text changes, and Low-Rank Adaptation (LoRA) on detection accuracy.<n>The findings highlight vulnerabilities in current detection methods and propose strategies to enhance the robustness and reliability of AI-image detection systems.
  arXiv  Detail & Related papers  (2024-11-26T06:35:26Z)
• MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
  The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored. We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
  arXiv  Detail & Related papers  (2024-09-15T13:08:59Z)
• Improving Interpretability and Robustness for the Detection of AI-Generated Images [6.116075037154215]
  We analyze existing state-of-the-art AIGI detection methods based on frozen CLIP embeddings. We show how to interpret them, shedding light on how images produced by various AI generators differ from real ones.
  arXiv  Detail & Related papers  (2024-06-21T10:33:09Z)
• RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
  RIGID is a training-free and model-agnostic method for robust AI-generated image detection. RIGID significantly outperforms existing trainingbased and training-free detectors.
  arXiv  Detail & Related papers  (2024-05-30T14:49:54Z)
• Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images [13.089550724738436]
  Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields. Their ability to create hyper-realistic images poses significant challenges in distinguishing between real and synthetic content. This work introduces a robust detection framework that integrates image and text features extracted by CLIP model with a Multilayer Perceptron (MLP) classifier.
  arXiv  Detail & Related papers  (2024-04-19T14:30:41Z)
• ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale [20.12991230544801]
  Generative image models have emerged as a promising technology to produce realistic images. There is growing demand to empower users to effectively discern and comprehend patterns of AI-generated images. We develop ASAP, an interactive visualization system that automatically extracts distinct patterns of AI-generated images.
  arXiv  Detail & Related papers  (2024-04-03T18:20:41Z)
• 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)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• 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.