The field of Fake Image Detection and Localization (FIDL) is highly fragmented, encompassing four domains: deepfake detection (Deepfake), image manipulation detection and localization (IMDL), artificial intelligence-generated image detection (AIGC), and document image manipulation localization (Doc). Although individual benchmarks exist in some domains, a unified benchmark for all domains in FIDL remains blank.

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 remarkable progress in neural-network-driven visual data generation, especially with neural rendering techniques like Neural Radiance Fields and 3DGaussian splatting, offers a powerful alternative to GANs and diffusion models. These methods can generate high-fidelity images and lifelike avatars, highlighting the need for robust detection methods. However, the lack of any large dataset containing images from neural rendering methods becomes a bottleneck for the detection of such sophisticated fake images. To address this limitation, we introduce NeuroRenderedFake, a comprehensive benchmark for evaluating emerging fake image detection methods. Our key contributions are threefold: (1) A large-scale dataset of fake images synthesized using state-of-the-art neural rendering techniques, significantly expanding the scope of fake image detection beyond generative models; (2) A cross-domain evaluation protocol designed to assess the domain gap and common artifacts between generative and neural rendering-based fake images; and (3) An in-depth spectral energy analysis that reveals how frequency domain characteristics influence the performance of fake image detectors. We train representative detectors, based on spatial, spectral, and multimodal architectures, on fake images generated by both generative and neural rendering models. We evaluate these detectors on 15 groups of fake images synthesized by cutting-edge neural rendering models, generative models, and combined methods that can exhibit artifacts from both domains. Additionally, we provide insightful findings through detailed experiments on degraded fake image detection and the impact of spectral features, aiming to advance research in this critical area.

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.