Detecting and Simulating Artifacts in GAN Fake Images (1907.06515v2)

Abstract: To detect GAN generated images, conventional supervised machine learning algorithms require collection of a number of real and fake images from the targeted GAN model. However, the specific model used by the attacker is often unavailable. To address this, we propose a GAN simulator, AutoGAN, which can simulate the artifacts produced by the common pipeline shared by several popular GAN models. Additionally, we identify a unique artifact caused by the up-sampling component included in the common GAN pipeline. We show theoretically such artifacts are manifested as replications of spectra in the frequency domain and thus propose a classifier model based on the spectrum input, rather than the pixel input. By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.

Detecting and Simulating Artifacts in GAN Fake Images: Analysis and Methodologies

The paper "Detecting and Simulating Artifacts in GAN Fake Images" presents a methodology to enhance the detection of images generated by Generative Adversarial Networks (GANs) by uncovering distinctive artifacts in the frequency domain. Through this approach, the authors reveal insights into the up-sampling component present in GAN models and propose innovative strategies for training classifiers without the need for extensive, specific GAN-generated datasets.

Key Contributions

1. Analysis of Up-sampling Artifacts: The paper identifies a unique artifact in GAN-generated images resulting from the up-sampling component within GAN pipelines. This artifact is characterized as spectrum replications in the frequency domain. The authors demonstrate that these artifacts provide a consistent signal that can be detected using frequency analysis, regardless of the GAN model used.
2. Spectrum-Based Classifier: By theorizing that spectrum replications manifest consistently across varied GAN outputs, the authors propose a classifier that operates on frequency spectrum inputs rather than pixel-level inputs. This classifier demonstrates improved accuracy in detecting GAN-generated images, even when trained on images from a single semantic category, as compared to traditional pixel-based classifiers.
3. AutoGAN Simulator: To navigate the challenge of not having access to specific GAN models or datasets of fake images for training, the paper introduces AutoGAN, a GAN simulator. AutoGAN emulates the typical pipeline shared by popular GAN models, creating simulated fake images from real images during training. This enables a classifier to distinguish real from GAN-generated images without direct exposure to the actual GAN outputs during its training.

Empirical Findings

The proposed methodology shows robust performance across various scenarios:

• The spectrum-based classifier achieves state-of-the-art detection accuracies on several GAN benchmarks, notably in dealing with well-known GAN models like CycleGAN.
• Across diverse experimental setups, including binary classification tasks with images from a single semantic category, the method demonstrates strong generalization abilities, handling unseen categories effectively.
• The AutoGAN model, while not having direct access to the generated output of specific GANs, furnishes a viable pathway to train classifiers that generalize well across different GAN outputs.
• The framework displays resilience in detection even with non-accessible models, making it a constructive approach for real-world applications where model details are unknown.

Implications and Future Directions

The implications of this research extend both theoretically and practically:

• Theoretical Insights: The research advances the understanding of GAN-generated image properties in the frequency domain, providing a replicable indicator (spectral replication artifact) for image forensics and robustness against potential GAN settings.
• Practical Applications: From a practical standpoint, this framework offers tools for industries grappling with synthetic media, from digital forensics to security sectors, facilitating more accurate distinguishing of real from artificial images.

Future work, as hinted in the paper, might delve into extending GAN simulator approaches towards the paper of additional processing modules beyond up-sampling. Additionally, further exploration into combating diverse types of post-processing, like compression and resampling, may enrich classifier robustness.

In conclusion, the proposed methods are significant in developing scalable solutions to identification challenges posed by GAN-generated images through the novel examination of image frequency spectra. This research lays groundwork for enhanced capabilities in image authenticity assessment without dependence on direct access to specific GAN model structures or outputs.