Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions (2003.01826v1)

Abstract: Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.

• The paper identifies that standard up-convolutions distort spectral properties, preventing GANs from faithfully replicating natural data distributions.
• The paper introduces a novel spectral regularization term that enhances training stability and improves image quality by mitigating frequency distortions.
• The paper demonstrates that spectral analysis can effectively detect deepfakes, achieving up to 100% accuracy on benchmark datasets.

Evaluating the Impact of Up-Convolutions on Spectral Properties in Generative Networks

The paper under review discusses a critical limitation in convolutional neural networks (CNNs), specifically in Generative Adversarial Networks (GANs), related to their capability to reproduce spectral distributions of natural training data accurately. The authors address a significant issue with up-convolution methods, which include up-sampling schemes such as transposed convolution commonly used in GANS, and their impact on generating realistic images or sequences. This analysis is grounded in both theoretical and empirical evaluation and suggests a solution through spectral regularization.

The core argument presented by the authors is that standard up-convolution processes impose spectral distortions on the generated outputs, leading to an inability to replicate the original spectral distributions correctly. By leveraging this flaw, they propose employing spectral analysis of the outputs to detect fake data with high accuracy. The paper reports achieving a detection accuracy of up to 100% on public benchmarks, demonstrating the problem's pervasiveness across different GAN architectures and datasets.

Key Findings and Contributions

1. Spectral Distortion Analysis: The authors highlight how common up-convolution strategies distort the spectral properties of generated images. Their experimental evidence is supplemented by theoretical analysis, demonstrating that these methods introduce discrepancies independent of the model architecture.
2. Proposal for Spectral Regularization: To mitigate these issues, a novel spectral regularization term is introduced in the loss function landscape of GANs. Empirical results suggest that adding this term improves not only the frequency fidelity of the outputs but also training stability, ultimately enhancing visual quality.
3. Implications for Deepfake Detection: A practical application proposed by this paper is deepfake detection, an area of growing concern with the rise of AI-generated media manipulations. The authors’ method, based on spectral analysis, outperforms more sophisticated detection algorithms, as demonstrated by experiments on different datasets, including Faces-HQ and FaceForensics++.
4. Training Stability and Model Robustness: Beyond enhancing the accuracy of the generated data, the spectral regularization approach appears to stabilize GAN training. This is evidenced by reduced instances of mode collapse and improved convergence metrics such as the Frechet Inception Distance (FID).

Implications and Future Directions

The work presents a robust critique of existing up-sampling operations within generative frameworks and provides a straightforward yet effective solution with broad implications. Practically, this methodology introduces a safeguard against the generation of ill-replicated training distributions, advancing the field toward developing more robust and reliable generative models. Theoretically, emphasizing spectral analysis opens avenues for further research into corrective methods and architectural innovations for frequency domain fidelity.

Future research could expand on this foundation by exploring alternative architectures or loss functions that inherently incorporate spectral considerations. Additionally, extending these findings to applications outside image generation, such as video synthesis or data compression, stands as a promising next step. As models continue to integrate into privacy-sensitive and security-critical environments, addressing such fundamental flaws in generative architectures is imperative.

In summary, this paper offers significant insights into the spectral inadequacies of existing up-sampling methods in deep generative networks. It proposes actionable solutions that reinforce both the visual quality and functional reliability of such models, thereby pushing the boundaries of generative methodologies in artificial intelligence.