Although deep neural networks have been widely applied in many application domains, they are found to be vulnerable to adversarial attacks. A recent promising set of attacking techniques have been proposed, which mainly focus on generating adversarial examples under digital-world settings. Such strategies are unfortunately not implementable for any physical-world scenarios such as autonomous driving. In this paper, we present FragGAN, a new GAN-based framework which is capable of generating an adversarial image which differs from the original input image only through replacing a targeted fragment within the image using a corresponding visually indistinguishable adversarial fragment. FragGAN ensures that the resulting entire image is effective in attacking. For any physical-world implementation, an attacker could physically print out the adversarial fragment and then paste it onto the original fragment (e.g., a roadside sign for autonomous driving scenarios). FragGAN also enables clean-label attacks against image classification, as the resulting attacks may succeed even without modifying any essential content of an image. Extensive experiments including physical-world case studies on state-of-the-art autonomous steering and image classification models demonstrate that FragGAN is highly effective and superior to simple extensions of existing approaches. To the best of our knowledge, FragGAN is the first approach that can implement effective and clean-label physical-world attacks.
GAN was first introduced in @cite_20 , implemented by a system of two neural networks contesting with each other in a zero-sum game framework. GAN is proved to be able to achieve visually appealing results in both face generation @cite_4 and manipulation @cite_37 . In order to further improve the quality of synthesis images, image-to-image GAN has been proposed such as the conditional GAN @cite_28 and the CycleGAN @cite_40 , which learn a loss function to train the mapping from the input image to the output image. @cite_6 presents AdvGAN, which leverages GAN to produce adversarial samples with high success rate. Different from these GAN-based approaches, focuses on generating a physically implementable adversarial image in which only those arbitrarily selected fragments in the original image are replaced using the generated adversarial fragments.
@cite_37 @cite_4 @cite_28 @cite_6 @cite_40 @cite_20
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This paper proposes an unpaired learning method for image enhancement. Given a set of photographs with the desired characteristics, the proposed method learns a photo enhancer which transforms an input image into an enhanced image with those characteristics. The method is based on the framework of two-way generative adversarial networks (GANs) with several improvements. First, we augment the U-Net with global features and show that it is more effective. The global U-Net acts as the generator in our GAN model. Second, we improve Wasserstein GAN (WGAN) with an adaptive weighting scheme. With this scheme, training converges faster and better, and is less sensitive to parameters than WGAN-GP. Finally, we propose to use individual batch normalization layers for generators in two-way GANs. It helps generators better adapt to their own input distributions. All together, they significantly improve the stability of GAN training for our application. Both quantitative and visual results show that the proposed method is effective for enhancing images. We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapsing problem and delivering state-of-the-art results. A minimax formulation was able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN. Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from. The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model. We term our method Mixture Generative Adversarial Nets (MGAN). We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators’ distributions and the empirical data distribution is minimal, whilst the JSD among generators’ distributions is maximal, hence effectively avoiding the mode collapsing problem. By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets. We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by the generators. The advance of Generative Adversarial Networks (GANs) enables realistic face image synthesis. However, synthesizing face images that preserve facial identity as well as have high diversity within each identity remains challenging. To address this problem, we present FaceFeat-GAN, a novel generative model that improves both image quality and diversity by using two stages. Unlike existing single-stage models that map random noise to image directly, our two-stage synthesis includes the first stage of diverse feature generation and the second stage of feature-to-image rendering. The competitions between generators and discriminators are carefully designed in both stages with different objective functions. Specially, in the first stage, they compete in the feature domain to synthesize various facial features rather than images. In the second stage, they compete in the image domain to render photo-realistic images that contain high diversity but preserve identity. Extensive experiments show that FaceFeat-GAN generates images that not only retain identity information but also have high diversity and quality, significantly outperforming previous methods. Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understandings on how GANs are able to map the latent code sampled from a random distribution to a photo-realistic image. Previous work assumes the latent space learned by GAN follows a distributed representation but observes the vector arithmetic phenomenon of the output's semantics in latent space. In this work, we interpret the semantics hidden in the latent space of well-trained GANs. We find that the latent code for well-trained generative models, such as ProgressiveGAN and StyleGAN, actually learns a disentangled representation after some linear transformations. We make a rigorous analysis on the encoding of various semantics in the latent space as well as their properties, and then study how these semantics are correlated to each other. Based on our analysis, we propose a simple and general technique, called InterFaceGAN, for semantic face editing in latent space. Given a synthesized face, we are able to faithfully edit its various attributes such as pose, expression, age, presence of eyeglasses, without retraining the GAN model. Furthermore, we show that even the artifacts occurred in output images are able to be fixed using same approach. Extensive results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable facial attribute representation