Minseon Kim

Jinsu Kim, Yunhun Nam, Minseon Kim et al.

Recent advances in text-to-image models have increased the exposure of powerful image editing techniques as a tool, raising concerns about their potential for malicious use. An emerging line of research to address such threats focuses on implanting "protective" adversarial noise into images before their public release, so future attempts to edit them using text-to-image models can be impeded. However, subsequent works have shown that these adversarial noises are often easily "reversed," e.g., with techniques as simple as JPEG compression, casting doubt on the practicality of the approach. In this paper, we argue that adversarial noise for image protection should not only be imperceptible, as has been a primary focus of prior work, but also irreversible, viz., it should be difficult to detect as noise provided that the original image is hidden. We propose a surprisingly simple method to enhance the robustness of image protection methods against noise reversal techniques. Specifically, it applies an adaptive per-region Gaussian blur on the noise to adjust the overall frequency spectrum. Through extensive experiments, we show that our method consistently improves the per-sample worst-case protection performance of existing methods against a wide range of reversal techniques on diverse image editing scenarios, while also reducing quality degradation due to noise in terms of perceptual metrics. Code is available at https://github.com/jsu-kim/BlurGuard.

Jihoon Tack, Sihyun Yu, Jongheon Jeong et al.

Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks. However, the phenomenon of robust overfitting, i.e., the robustness starts to decrease significantly during AT, has been problematic, not only making practitioners consider a bag of tricks for a successful training, e.g., early stopping, but also incurring a significant generalization gap in the robustness. In this paper, we propose an effective regularization technique that prevents robust overfitting by optimizing an auxiliary `consistency' regularization loss during AT. Specifically, we discover that data augmentation is a quite effective tool to mitigate the overfitting in AT, and develop a regularization that forces the predictive distributions after attacking from two different augmentations of the same instance to be similar with each other. Our experimental results demonstrate that such a simple regularization technique brings significant improvements in the test robust accuracy of a wide range of AT methods. More remarkably, we also show that our method could significantly help the model to generalize its robustness against unseen adversaries, e.g., other types or larger perturbations compared to those used during training. Code is available at https://github.com/alinlab/consistency-adversarial.

Hyomin Lee, Minseon Kim, Sangwon Jang et al.

Variational Autoencoders (VAEs) have played a key role in scaling up diffusion-based generative models, as in Stable Diffusion, yet questions regarding their robustness remain largely underexplored. Although adversarial training has been an established technique for enhancing robustness in predictive models, it has been overlooked for generative models due to concerns about potential fidelity degradation by the nature of trade-offs between performance and robustness. In this work, we challenge this presumption, introducing Smooth Robust Latent VAE (SRL-VAE), a novel adversarial training framework that boosts both generation quality and robustness. In contrast to conventional adversarial training, which focuses on robustness only, our approach smooths the latent space via adversarial perturbations, promoting more generalizable representations while regularizing with originality representation to sustain original fidelity. Applied as a post-training step on pre-trained VAEs, SRL-VAE improves image robustness and fidelity with minimal computational overhead. Experiments show that SRL-VAE improves both generation quality, in image reconstruction and text-guided image editing, and robustness, against Nightshade attacks and image editing attacks. These results establish a new paradigm, showing that adversarial training, once thought to be detrimental to generative models, can instead enhance both fidelity and robustness.