DIRE for Diffusion-Generated Image Detection (2303.09295v1)

Abstract: Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusion-generated images. We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data. To address this issue, we propose a novel image representation called DIffusion Reconstruction Error (DIRE), which measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. We observe that diffusion-generated images can be approximately reconstructed by a diffusion model while real images cannot. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images. DIRE provides an effective way to detect images generated by most diffusion models, and it is general for detecting generated images from unseen diffusion models and robust to various perturbations. Furthermore, we establish a comprehensive diffusion-generated benchmark including images generated by eight diffusion models to evaluate the performance of diffusion-generated image detectors. Extensive experiments on our collected benchmark demonstrate that DIRE exhibits superiority over previous generated-image detectors. The code and dataset are available at https://github.com/ZhendongWang6/DIRE.

• The paper introduces DIRE as an image representation that uses reconstruction error to distinguish real images from those generated by diffusion models.
• It employs a two-step process by inverting images to noise vectors and reconstructing them with a pre-trained diffusion model to compute error metrics.
• Extensive experiments on the DiffusionForensics benchmark demonstrate DIRE’s superior accuracy and robustness compared to state-of-the-art forensic methods.

Detailed Analysis of DIRE for Diffusion-Generated Image Detection

The paper "DIRE for Diffusion-Generated Image Detection" introduces a novel approach to the detection of images generated by diffusion models, a topic of growing concern given the increasing capabilities of these models in creating high-quality synthetic images. As diffusion models advance, so does the potential for misuse in privacy violations and malicious deepfake technologies. This paper seeks to address these challenges by developing a sophisticated detection mechanism based on a representation termed as DIffusion Reconstruction Error (DIRE).

Core Contributions and Methodology

The central contribution of this work is the introduction of DIRE as an image representation for distinguishing between real and diffusion-generated images. The authors propose that the discrepancies between an input image and its reconstruction, processed through a diffusion model, can serve as a critical indicator for identifying generated images. Diffusion-generated images exhibit lower reconstruction errors due to their origination within the diffusion model's generation space, whereas real images generate higher reconstruction errors during the reconstruction process.

The technique involves two processes: inverting an image to a noise vector and then reconstructing it using a pre-trained diffusion model. By calculating the error between the original image and its reconstructed counterpart, the DIRE serves as a robust feature for classification. This feature is pivotal as it exploits the inherent qualities of diffusion-model-generated artifacts.

To evaluate the efficacy of their approach, the authors have established a benchmark dataset named DiffusionForensics. This dataset contains images from eight different diffusion models, encompassing various types of generation scenarios including unconditional, conditional, and text-to-image generation tasks.

Empirical Results

The robustness of DIRE is affirmed through extensive experimentation. The methodology exhibits remarkable generalization capabilities across unseen diffusion models and various perturbations. The DIRE representation demonstrates superiority compared to state-of-the-art digital image forensics methods, particularly evidencing high accuracy and precision. The paper reports strong numerical outcomes, embedding confidence in the generalizability of their detector especially in scenarios with diffusion-generated images from models unexposed during training.

Practical and Theoretical Implications

From a theoretical standpoint, this research underscores the potential of leveraging reconstruction errors as a discriminative feature in image forensics. This insight can drive future exploration into more nuanced and sophisticated features stemming from generative models. Practically, the method offers a viable solution to improve automated systems in identifying AI-generated content, thereby assisting in mitigating privacy risks and fraudulent activities associated with deepfake media.

Future Directions

The implications of this work could extend into more generalized frameworks where similar principles may be applied beyond diffusion models, potentially addressing other generative models under the growing family of AI-generated content. Further investigation into hybrid models that incorporate DIRE with other feature sets could optimize detection performance across diverse model architectures.

In conclusion, the paper introduces a profound step toward securing authenticity in digital media with significant implications for both current applications and future research trajectories in AI-generated media detection.