Mixture of Low-rank Experts for Transferable AI-Generated Image Detection

• URL: http://arxiv.org/abs/2404.04883v1
• Date: Sun, 7 Apr 2024 09:01:50 GMT
• Title: Mixture of Low-rank Experts for Transferable AI-Generated Image Detection
• Authors: Zihan Liu, Hanyi Wang, Yaoyu Kang, Shilin Wang,
• Abstract summary: Generative models have shown a giant leap in photo-realistic images with minimal expertise, sparking concerns about the authenticity of online information. This study aims to develop a universal AI-generated image detector capable of identifying images from diverse sources. Inspired by the zero-shot transferability of pre-trained vision-language models, we seek to harness the non-trivial visual-world knowledge and descriptive proficiency of CLIP-ViT to generalize over unknown domains.
• Score: 18.631006488565664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models have shown a giant leap in synthesizing photo-realistic images with minimal expertise, sparking concerns about the authenticity of online information. This study aims to develop a universal AI-generated image detector capable of identifying images from diverse sources. Existing methods struggle to generalize across unseen generative models when provided with limited sample sources. Inspired by the zero-shot transferability of pre-trained vision-language models, we seek to harness the nontrivial visual-world knowledge and descriptive proficiency of CLIP-ViT to generalize over unknown domains. This paper presents a novel parameter-efficient fine-tuning approach, mixture of low-rank experts, to fully exploit CLIP-ViT's potential while preserving knowledge and expanding capacity for transferable detection. We adapt only the MLP layers of deeper ViT blocks via an integration of shared and separate LoRAs within an MoE-based structure. Extensive experiments on public benchmarks show that our method achieves superiority over state-of-the-art approaches in cross-generator generalization and robustness to perturbations. Remarkably, our best-performing ViT-L/14 variant requires training only 0.08% of its parameters to surpass the leading baseline by +3.64% mAP and +12.72% avg.Acc across unseen diffusion and autoregressive models. This even outperforms the baseline with just 0.28% of the training data. Our code and pre-trained models will be available at https://github.com/zhliuworks/CLIPMoLE.

Related papers

• 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)
• Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities [88.398085358514]
  Contrastive Deepfake Embeddings (CoDE) is a novel embedding space specifically designed for deepfake detection. CoDE is trained via contrastive learning by additionally enforcing global-local similarities.
  arXiv  Detail & Related papers  (2024-07-29T18:00:10Z)
• Model Inversion Attacks Through Target-Specific Conditional Diffusion Models [54.69008212790426]
  Model attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. We propose Diffusion-based Model Inversion (Diff-MI) attacks to alleviate these issues.
  arXiv  Detail & Related papers  (2024-07-16T06:38:49Z)
• DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception [66.88792390480343]
  We propose DEEM, a simple but effective approach that utilizes the generative feedback of diffusion models to align the semantic distributions of the image encoder. DEEM exhibits enhanced robustness and a superior capacity to alleviate model hallucinations while utilizing fewer trainable parameters, less pre-training data, and a smaller base model size.
  arXiv  Detail & Related papers  (2024-05-24T05:46:04Z)
• Bi-LORA: A Vision-Language Approach for Synthetic Image Detection [14.448350657613364]
  Deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs) have ushered in an era of generating highly realistic images. This paper takes inspiration from the potent convergence capabilities between vision and language, coupled with the zero-shot nature of vision-language models (VLMs) We introduce an innovative method called Bi-LORA that leverages VLMs, combined with low-rank adaptation (LORA) tuning techniques, to enhance the precision of synthetic image detection for unseen model-generated images.
  arXiv  Detail & Related papers  (2024-04-02T13:54:22Z)
• CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection [3.849401956130233]
  We explore the effectiveness of pre-trained vision-language models (VLMs) when paired with recent adaptation methods for universal deepfake detection. We employ only a single dataset (ProGAN) in order to adapt CLIP for deepfake detection. The simple and lightweight Prompt Tuning based adaptation strategy outperforms the previous SOTA approach by 5.01% mAP and 6.61% accuracy.
  arXiv  Detail & Related papers  (2024-02-20T11:26:42Z)
• Raising the Bar of AI-generated Image Detection with CLIP [50.345365081177555]
  The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide variety of challenging scenarios.
  arXiv  Detail & Related papers  (2023-11-30T21:11:20Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion [22.237426507711362]
  Model-Agnostic Zero-Shot Classification (MA-ZSC) refers to training non-specific classification architectures to classify real images without using any real images during training. Recent research has demonstrated that generating synthetic training images using diffusion models provides a potential solution to address MA-ZSC. We propose modifications to the text-to-image generation process using a pre-trained diffusion model to enhance diversity.
  arXiv  Detail & Related papers  (2023-02-07T07:13:53Z)
• Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample [6.243995448840211]
  We present a novel non-adversarial generative method - Clustered Optimization of LAtent space (COLA) In the full data regime, our method is capable of generating diverse multi-class images with no supervision. In the small-data regime, where only a small sample of labeled images is available for training with no access to additional unlabeled data, our results surpass state-of-the-art GAN models trained on the same amount of data.
  arXiv  Detail & Related papers  (2020-11-25T18:47:27Z)

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.