2022-05-29 更新
Misleading Deep-Fake Detection with GAN Fingerprints
Authors:Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring
Generative adversarial networks (GANs) have made remarkable progress in synthesizing realistic-looking images that effectively outsmart even humans. Although several detection methods can recognize these deep fakes by checking for image artifacts from the generation process, multiple counterattacks have demonstrated their limitations. These attacks, however, still require certain conditions to hold, such as interacting with the detection method or adjusting the GAN directly. In this paper, we introduce a novel class of simple counterattacks that overcomes these limitations. In particular, we show that an adversary can remove indicative artifacts, the GAN fingerprint, directly from the frequency spectrum of a generated image. We explore different realizations of this removal, ranging from filtering high frequencies to more nuanced frequency-peak cleansing. We evaluate the performance of our attack with different detection methods, GAN architectures, and datasets. Our results show that an adversary can often remove GAN fingerprints and thus evade the detection of generated images.
PDF In IEEE Deep Learning and Security Workshop (DLS) 2022
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Enriching StyleGAN with Illumination Physics
Authors:Anand Bhattad, D. A. Forsyth
StyleGAN generates novel images of a scene from latent codes which are impressively disentangled. But StyleGAN generates images that are “like” its training set. This paper shows how to use simple physical properties of images to enrich StyleGAN’s generation capacity. We use an intrinsic image method to decompose an image, then search the latent space of a pretrained StyleGAN to find novel directions that fix one component (say, albedo) and vary another (say, shading). Therefore, we can change the lighting of a complex scene without changing the scene layout, object colors, and shapes. Or we can change the colors of objects without changing shading intensity or their scene layout. Our experiments suggest the proposed method, StyLitGAN, can add and remove luminaires in the scene and generate images with realistic lighting effects — cast shadows, soft shadows, inter-reflections, glossy effects — requiring no labeled paired relighting data or any other geometric supervision. Qualitative evaluation confirms that our generated images are realistic and that we can change or fix components at will. Quantitative evaluation shows that pre-trained StyleGAN could not produce the images StyLitGAN produces; we can automatically generate realistic out-of-distribution images, and so can significantly enrich the range of images StyleGAN can produce.
PDF Project page: https://anandbhattad.github.io/projects/StyLitGAN/
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Accelerating Diffusion Models via Early Stop of the Diffusion Process
Authors:Zhaoyang Lyu, Xudong XU, Ceyuan Yang, Dahua Lin, Bo Dai
Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressive performance on various generation tasks. By modeling the reverse process of gradually diffusing the data distribution into a Gaussian distribution, generating a sample in DDPMs can be regarded as iteratively denoising a randomly sampled Gaussian noise. However, in practice DDPMs often need hundreds even thousands of denoising steps to obtain a high-quality sample from the Gaussian noise, leading to extremely low inference efficiency. In this work, we propose a principled acceleration strategy, referred to as Early-Stopped DDPM (ES-DDPM), for DDPMs. The key idea is to stop the diffusion process early where only the few initial diffusing steps are considered and the reverse denoising process starts from a non-Gaussian distribution. By further adopting a powerful pre-trained generative model, such as GAN and VAE, in ES-DDPM, sampling from the target non-Gaussian distribution can be efficiently achieved by diffusing samples obtained from the pre-trained generative model. In this way, the number of required denoising steps is significantly reduced. In the meantime, the sample quality of ES-DDPM also improves substantially, outperforming both the vanilla DDPM and the adopted pre-trained generative model. On extensive experiments across CIFAR-10, CelebA, ImageNet, LSUN-Bedroom and LSUN-Cat, ES-DDPM obtains promising acceleration effect and performance improvement over representative baseline methods. Moreover, ES-DDPM also demonstrates several attractive properties, including being orthogonal to existing acceleration methods, as well as simultaneously enabling both global semantic and local pixel-level control in image generation.
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Analyzing the Latent Space of GAN through Local Dimension Estimation
Authors:Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang
The impressive success of style-based GANs (StyleGANs) in high-fidelity image synthesis has motivated research to understand the semantic properties of their latent spaces. Recently, a close relationship was observed between the semantically disentangled local perturbations and the local PCA components in the learned latent space $\mathcal{W}$. However, understanding the number of disentangled perturbations remains challenging. Building upon this observation, we propose a local dimension estimation algorithm for an arbitrary intermediate layer in a pre-trained GAN model. The estimated intrinsic dimension corresponds to the number of disentangled local perturbations. In this perspective, we analyze the intermediate layers of the mapping network in StyleGANs. Our analysis clarifies the success of $\mathcal{W}$-space in StyleGAN and suggests an alternative. Moreover, the intrinsic dimension estimation opens the possibility of unsupervised evaluation of global-basis-compatibility and disentanglement for a latent space. Our proposed metric, called Distortion, measures an inconsistency of intrinsic tangent space on the learned latent space. The metric is purely geometric and does not require any additional attribute information. Nevertheless, the metric shows a high correlation with the global-basis-compatibility and supervised disentanglement score. Our findings pave the way towards an unsupervised selection of globally disentangled latent space among the intermediate latent spaces in a GAN.
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Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
Authors:Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
Deep generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution generation to avoid spurious sample generation, thereby limiting their creativity. Thus, incorporating research on human creativity into generative deep learning techniques presents an opportunity to make their outputs more compelling and human-like. As we see the emergence of generative models directed toward creativity research, a need for machine learning-based surrogate metrics to characterize creative output from these models is imperative. We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models. Our experiments on the standard image benchmarks, and their “creatively generated” variants, reveal that the proposed subset scores distribution is more useful for detecting creative processes in the activation space rather than the pixel space. Further, we found that creative samples generate larger subsets of anomalies than normal or non-creative samples across datasets. The node activations highlighted during the creative decoding process are different from those responsible for the normal sample generation. Lastly, we assess if the images from the subsets selected by our method were also found creative by human evaluators, presenting a link between creativity perception in humans and node activations within deep neural nets.
PDF Accepted to IJCAI 2022 - Creativity Track - Extended version from Synthetic Data Generation Workshop at ICLR’21 submission (arXiv:2104.00479). arXiv admin note: text overlap with arXiv:2105.12479
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