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1. Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks

   Lin, Wei-An; Pong Lau, Chun; Levine, Alexander; Chellappa, Rama; Feizi, Soheil (January 2020, Advances in Neural Information Processing Systems Foundation (NeurIPS))

   null (Ed.)

   Adversarial training is a popular defense strategy against attack threat models with bounded Lp norms. However, it often degrades the model performance on normal images and the defense does not generalize well to novel attacks. Given the success of deep generative models such as GANs and VAEs in characterizing the underlying manifold of images, we investigate whether or not the aforementioned problems can be remedied by exploiting the underlying manifold information. To this end, we construct an "On-Manifold ImageNet" (OM-ImageNet) dataset by projecting the ImageNet samples onto the manifold learned by StyleGSN. For this dataset, the underlying manifold information is exact. Using OM-ImageNet, we first show that adversarial training in the latent space of images improves both standard accuracy and robustness to on-manifold attacks. However, since no out-of-manifold perturbations are realized, the defense can be broken by Lp adversarial attacks. We further propose Dual Manifold Adversarial Training (DMAT) where adversarial perturbations in both latent and image spaces are used in robustifying the model. Our DMAT improves performance on normal images, and achieves comparable robustness to the standard adversarial training against Lp attacks. In addition, we observe that models defended by DMAT achieve improved robustness against novel attacks which manipulate images by global color shifts or various types of image filtering. Interestingly, similar improvements are also achieved when the defended models are tested on out-of-manifold natural images. These results demonstrate the potential benefits of using manifold information in enhancing robustness of deep learning models against various types of novel adversarial attacks. 

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2. PCA as a defense against some adversaries

   Gupte, Aparna; Banburski, Andrzej; Poggio, Tomaso (March 2022, Center for Brains, Minds and Machines (CBMM))

   Neural network classifiers are known to be highly vulnerable to adversarial perturbations in their inputs. Under the hypothesis that adversarial examples lie outside of the sub-manifold of natural images, previous work has investigated the impact of principal components in data on adversarial robustness. In this paper we show that there exists a very simple defense mechanism in the case where adversarial images are separable in a previously defined $(k,p)$ metric. This defense is very successful against the popular Carlini-Wagner attack, but less so against some other common attacks like FGSM. It is interesting to note that the defense is still successful for relatively large perturbations. 

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3. Explore the Transformation Space for Adversarial Images

   Chen, Jiyu; Wang, David; and Chen, Hao (March 2020, ACM Conference on Data and Application Security and Privacy)

   Deep learning models are vulnerable to adversarial examples. Most of current adversarial attacks add pixel-wise perturbations restricted to some L^p-norm, and defense models are evaluated also on adversarial examples restricted inside L^p-norm balls. However, we wish to explore adversarial examples exist beyond L^p-norm balls and their implications for attacks and defenses. In this paper, we focus on adversarial images generated by transformations. We start with color transformation and propose two gradient-based attacks. Since L^p-norm is inappropriate for measuring image quality in the transformation space, we use the similarity between transformations and the Structural Similarity Index. Next, we explore a larger transformation space consisting of combinations of color and affine transformations. We evaluate our transformation attacks on three data sets --- CIFAR10, SVHN, and ImageNet --- and their corresponding models. Finally, we perform retraining defenses to evaluate the strength of our attacks. The results show that transformation attacks are powerful. They find high-quality adversarial images that have higher transferability and misclassification rates than C&W's L^p attacks, especially at high confidence levels. They are also significantly harder to defend against by retraining than C&W's L^p attacks. More importantly, exploring different attack spaces makes it more challenging to train a universally robust model. 

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4. One Man’s Trash is Another Man’s Treasure: Resisting Adversarial Examples by Adversarial Examples

   Xiao, Chang; Zheng, Changxi (June 2020, IEEE Conference on Computer Vision and Pattern Recognition)

   Modern image classification systems are often built on deep neural networks, which suffer from adversarial examples—images with deliberately crafted, imperceptible noise to mislead the network’s classification. To defend against adversarial examples, a plausible idea is to obfuscate the network’s gradient with respect to the input image. This general idea has inspired a long line of defense methods. Yet, almost all of them have proven vulnerable. We revisit this seemingly flawed idea from a radically different perspective. We embrace the omnipresence of adversarial examples and the numerical procedure of crafting them, and turn this harmful attacking process into a useful defense mechanism. Our defense method is conceptually simple: before feeding an input image for classification, transform it by finding an adversarial example on a pre- trained external model. We evaluate our method against a wide range of possible attacks. On both CIFAR-10 and Tiny ImageNet datasets, our method is significantly more robust than state-of-the-art methods. Particularly, in comparison to adversarial training, our method offers lower training cost as well as stronger robustness. 

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5. Compression to the Rescue: Defending from Adversarial Attacks Across Modalities

   Das, Nilaksh; Shanbhogue, Madhuri; Chen, Shang-Tse; Hohman, Fred; Li, Siwei; Chen, Li; Kounavis, Michael E.; Chau, Duen Horng (August 2018, ACM SIGKDD Conference on Knowledge Discovery and Data Mining)

   Research in the upcoming field of adversarial ML has revealed that machine learning, especially deep learning, is highly vulnerable to imperceptible adversarial perturbations, both in the domain of vision as well as speech. This has induced an urgent need to devise fast and practical approaches to secure deep learning models from adversarial attacks, so that they can be safely deployed in real-world applications. In this showcase, we put forth the idea of compression as a viable solution to defend against adversarial attacks across modalities. Since most of these attacks depend on the gradient of the model to craft an adversarial instance, compression, which is usually non-differentiable, denies a useful gradient to the attacker. In the vision domain we have JPEG compression, and in the audio domain we have MP3 compression and AMR encoding -- all widely adopted techniques that have very fast implementations on most platforms, and can be feasibly leveraged as defenses. We will show the effectiveness of these techniques against adversarial attacks through live demonstrations, both for vision as well as speech. These demonstrations would include real-time computation of adversarial perturbations for images and audio, as well as interactive application of compression for defense. We would invite and encourage the audience to experiment with their own images and audio samples during the demonstrations. This work was undertaken jointly by researchers from Georgia Institute of Technology and Intel Corporation. 

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