Rectified flow is a generative model that learns smooth transport mappings between two distributions through an ordinary differential equation (ODE). The model learns a straight ODE by reflow steps which iteratively update the supervisory flow. It allows for a relatively simple and efficient generation of high-quality images. However, rectified flow still faces several challenges. 1) The reflow process is slow because it requires a large number of generated pairs to model the target distribution.

The rapid increase in AI-generated images (AIGIs) underscores the need for detection methods. Existing detectors are often trained on biased datasets, leading to overfitting on spurious correlations between non-causal image attributes and real/synthetic labels. While these biased features enhance performance on the training data, they result in substantial performance degradation when tested on unbiased datasets. A common solution is to perform data alignment through generative reconstruction, matching the content between real and synthetic images. However, we find that pixel-level alignment alone is inadequate, as the reconstructed images still suffer from frequency-level misalignment, perpetuating spurious correlations.

By leveraging neural rendering technologies based on NeRF and 3DGS, we create a wide array of realistic 3D scene representations and generate a multitude of synthesized 2D images from different perspectives. Moreover, through the combination of generative models with these advanced neural rendering methods, we generate highly sophisticated but fake images that incorporate combined artifacts. Unlike other existing datasets that largely focus on fake images generated by traditional generative models such as GANs or diffusion models, our NeuroRenderedFake dataset significantly extends the boundaries of a much-needed dataset for sophisticated fake image detection. This benchmark consists of over 2 million images, i.e., 512,972 authentic images and 1,653,881 highly sophisticated fake images. Therefore, it can serve as the largest collection of diverse images generated through advanced synthesis and neural rendering techniques. This work is expected to have a significant positive societal impact, particularly benefiting the forensic community and media outlets. Our method can enhance the accurate and timely identification of real-look-like but fake images that are often found in our mailboxes or social media platforms. The development of accurate techniques to detect these images is crucial for addressing concerns related to security, privacy, and preserving harmony within our community.

The increasing realism of synthetic images generated by advanced models such as VAEs, GANs, and LDMs poses significant challenges for synthetic image detection. To address this issue, we explore two artifact types introduced during the generation process: (1) latent distribution deviations and (2) decoding-induced smoothing effects, which manifest as inconsistencies in local textures, edges, and color transitions. Leveraging local pixel dependencies (LPD) properties rooted in Markov Random Fields, we reconstruct synthetic images using neighboring pixel information to expose disruptions in texture continuity and edge coherence. Building upon LPD, we propose FerretNet, a lightweight neural network with only 1.1M parameters that delivers efficient and robust synthetic image detection. Extensive experiments demonstrate that FerretNet--trained exclusively on the 4class ProGAN dataset--achieves an average accuracy of 97.1% on an open-world benchmark comprising 22 generative models.

The real image, which the police station has since shared, shows the officers in normal clothes and no female officer in the picture at all. The real image, which the police station has since shared, shows the officers in normal clothes and no female officer in the picture at all. Picture was created by administrator in charge of station's Facebook account who wanted to create'friendlier image' It was an arresting image and an irresistible story. A group of tough Thai police officers - five men and one woman - all wearing elaborate festival-style dresses, surrounding a drug dealer they had caught while undercover. The image, released by local police, was so compelling that it found its way on to the front page of the UK's Daily Star, as well as in picture stories in the Telegraph, the Sun and the New York Post. The Sun wrote: "The burly crew of five men and one woman slipped into skin tight sequins and feathers for the covert mission in Thailand ."

The extraordinary ability of generative models to generate photographic images has intensified concerns about the spread of disinformation, thereby leading to the demand for detectors capable of distinguishing between AI-generated fake images and real images. However, the lack of large datasets containing images from the most advanced image generators poses an obstacle to the development of such detectors. In this paper, we introduce the GenImage dataset, which has the following advantages: 1) Plenty of Images, including over one million pairs of AI-generated fake images and collected real images.

We use the images at 256 256resolution. We follow [21] and use all the images for training. The images used for the qualitative visualizations contain random images from the web and samples from CelebA-HQ. AFHQ [8] 15,000high quality images categorized into three domains: cat, dog and wildlife. We use the images at 128 128 resolution, holding out 500 images from each domain for testing.

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