CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI
- URL: http://arxiv.org/abs/2503.18286v1
- Date: Mon, 24 Mar 2025 01:59:29 GMT
- Title: CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI
- Authors: Siyuan Cheng, Lingjuan Lyu, Zhenting Wang, Xiangyu Zhang, Vikash Sehwag,
- Abstract summary: Current efforts to distinguish between real and AI-generated images may lack generalization.<n>We propose a novel framework, Co-Spy, that first enhances existing semantic features.<n>We also create Co-Spy-Bench, a comprehensive dataset comprising 5 real image datasets and 22 state-of-the-art generative models.
- Score: 58.35348718345307
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the rapid advancement of generative AI, it is now possible to synthesize high-quality images in a few seconds. Despite the power of these technologies, they raise significant concerns regarding misuse. Current efforts to distinguish between real and AI-generated images may lack generalization, being effective for only certain types of generative models and susceptible to post-processing techniques like JPEG compression. To overcome these limitations, we propose a novel framework, Co-Spy, that first enhances existing semantic features (e.g., the number of fingers in a hand) and artifact features (e.g., pixel value differences), and then adaptively integrates them to achieve more general and robust synthetic image detection. Additionally, we create Co-Spy-Bench, a comprehensive dataset comprising 5 real image datasets and 22 state-of-the-art generative models, including the latest models like FLUX. We also collect 50k synthetic images in the wild from the Internet to enable evaluation in a more practical setting. Our extensive evaluations demonstrate that our detector outperforms existing methods under identical training conditions, achieving an average accuracy improvement of approximately 11% to 34%. The code is available at https://github.com/Megum1/Co-Spy.
Related papers
- Time Step Generating: A Universal Synthesized Deepfake Image Detector [0.4488895231267077]
We propose a universal synthetic image detector Time Step Generating (TSG)
TSG does not rely on pre-trained models' reconstructing ability, specific datasets, or sampling algorithms.
We test the proposed TSG on the large-scale GenImage benchmark and it achieves significant improvements in both accuracy and generalizability.
arXiv Detail & Related papers (2024-11-17T09:39:50Z) - 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) - A Sanity Check for AI-generated Image Detection [49.08585395873425]
We propose AIDE (AI-generated Image DEtector with Hybrid Features) to detect AI-generated images.<n>AIDE achieves +3.5% and +4.6% improvements to state-of-the-art methods.
arXiv Detail & Related papers (2024-06-27T17:59:49Z) - SIDBench: A Python Framework for Reliably Assessing Synthetic Image Detection Methods [9.213926755375024]
The creation of completely synthetic images presents a unique challenge.
There is often a large gap between experimental results on benchmark datasets and the performance of methods in the wild.
This paper introduces a benchmarking framework that integrates several state-of-the-art SID models.
arXiv Detail & Related papers (2024-04-29T09:50:16Z) - Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection [13.840950434728533]
State-of-the-art Synthetic Image Detection (SID) research has led to strong evidence on the advantages of feature extraction from foundation models.
We leverage the image representations extracted by intermediate Transformer blocks of CLIP's image-encoder via a lightweight network.
Our method is compared against the state-of-the-art by evaluating it on 20 test datasets and exhibits an average +10.6% absolute performance improvement.
arXiv Detail & Related papers (2024-02-29T12:18:43Z) - 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) - Is synthetic data from generative models ready for image recognition? [69.42645602062024]
We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks.
We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
arXiv Detail & Related papers (2022-10-14T06:54:24Z) - A Shared Representation for Photorealistic Driving Simulators [83.5985178314263]
We propose to improve the quality of generated images by rethinking the discriminator architecture.
The focus is on the class of problems where images are generated given semantic inputs, such as scene segmentation maps or human body poses.
We aim to learn a shared latent representation that encodes enough information to jointly do semantic segmentation, content reconstruction, along with a coarse-to-fine grained adversarial reasoning.
arXiv Detail & Related papers (2021-12-09T18:59:21Z)
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.