On the detection of synthetic images generated by diffusion models (2211.00680v1)

Abstract: Over the past decade, there has been tremendous progress in creating synthetic media, mainly thanks to the development of powerful methods based on generative adversarial networks (GAN). Very recently, methods based on diffusion models (DM) have been gaining the spotlight. In addition to providing an impressive level of photorealism, they enable the creation of text-based visual content, opening up new and exciting opportunities in many different application fields, from arts to video games. On the other hand, this property is an additional asset in the hands of malicious users, who can generate and distribute fake media perfectly adapted to their attacks, posing new challenges to the media forensic community. With this work, we seek to understand how difficult it is to distinguish synthetic images generated by diffusion models from pristine ones and whether current state-of-the-art detectors are suitable for the task. To this end, first we expose the forensics traces left by diffusion models, then study how current detectors, developed for GAN-generated images, perform on these new synthetic images, especially in challenging social-networks scenarios involving image compression and resizing. Datasets and code are available at github.com/grip-unina/DMimageDetection.

On the Detection of Synthetic Images Generated by Diffusion Models

The paper "On the detection of synthetic images generated by diffusion models," authored by Riccardo Corvi and colleagues, addresses the emergent challenge of detecting images synthesized by diffusion models (DMs), a new frontier in generative technology that is establishing a profound impact across various domains. The authors provide a comprehensive analysis aimed at discerning how well current detection methodologies, originally developed for GAN-generated images, perform against these novel generative models.

Overview

The paper opens with an acknowledgment of the recent advancements in generating high-quality synthetic media, especially through diffusion models, which have gained significant traction over traditional generative adversarial networks (GANs). While these models present new opportunities for creative industries, they also pose significant risks in the form of malfeasance, such as disinformation. The paper therefore seeks to evaluate the forensic reliability of existing detectors against diffusion model-generated images (DMI).

Methodology

The authors undertake a detailed approach to assess whether state-of-the-art detectors can successfully distinguish DMI from genuine images. Their methodology includes:

1. Forensic Tracing: Identifying and evaluating the unique forensic traces left by diffusion models.
2. Generalization Testing: Examining whether detectors trained solely on images from one architecture, such as ProGAN or Latent Diffusion, can successfully identify images from unseen architectures.
3. Robustness Analysis: Testing detectors under realistic conditions involving resizing and compression typical in social media platforms.

Key Findings

The paper reveals several insights that are critical for the advancement of synthetic image detection:

• Forensic Artifacts: Diffusion models, like GANs, leave distinct, albeit subtle, forensic traces. However, the strength and visibility of these traces vary significantly across different diffusion architectures.
• Detector Performance: When tested on different datasets, state-of-the-art detectors, such as those based on CNN architectures, exhibit varying performance. Detectors often struggle when tasked with recognizing images from architectures not included in their training set, signifying a limitation in their generalization capability.
• Impact of Preprocessing: A vital finding is that preprocessing operations such as compression and resizing can degrade detection performance significantly. This highlights the vulnerability of existing methods in practical scenarios where social media platforms constantly manipulate image size and quality.

Implications and Future Work

The implications of this research stretch across both forensic technology and artificial intelligence theory. Practically, understanding and improving the current detection mechanisms can augment tools available for combating visual misinformation. Theoretically, these findings provoke further investigation into the characteristics of diffusion models that elude current detection capabilities.

Future work, as suggested by the authors, should explore the nuances of diffusion model fingerprints, aiming to enhance detection robustness and scalability. Moreover, adaptive learning techniques could be explored to improve generalization, enabling detectors to learn from a diverse palette of synthetic architectures without prior exhaustive exposure.

In conclusion, Corvi et al. provide a critical examination of the efficacy of current synthetic image detectors in the context of emerging diffusion models, shedding light on existing gaps and paving the way for further research and development in the field of visual forensics.