Collaborating Authors

Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients Machine Learning

We focus our attention on the problem of generating adversarial perturbations based on the gradient in image classification domain; substantial pixel perturbations make features learned by deep neural networks changed in clean images which fool deep neural models into making incorrect predictions. However, large-scale pixel modification in the image directly makes the changes visible although attack process success. To find the optimal perturbations which can quantify the boundary distance directly between clean images and adversarial examples in latent space, we propose a novel method for integrated adversarial perturbations generation, which formulate adversarial perturbations in the adaptive and integrated gradient level. Our approach calls few adaptive gradient operators to seek the decision boundary between original images and corresponding adversarial examples directly. We compare our proposed method for crafting adversarial perturbations with other state-of-the-art gradient-based attack methods. Experimental results suggest that adversarial samples generated by our approach show excellent efficiency in fooling deep neural classification networks with lower pixel modification and good transferability on image classification models.

Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks Machine Learning

DNN is presenting human-level performance for many complex intelligent tasks in real-world applications. However, it also introduces ever-increasing security concerns. For example, the emerging adversarial attacks indicate that even very small and often imperceptible adversarial input perturbations can easily mislead the cognitive function of deep learning systems (DLS). Existing DNN adversarial studies are narrowly performed on the ideal software-level DNN models with a focus on single uncertainty factor, i.e. input perturbations, however, the impact of DNN model reshaping on adversarial attacks, which is introduced by various hardware-favorable techniques such as hash-based weight compression during modern DNN hardware implementation, has never been discussed. In this work, we for the first time investigate the multi-factor adversarial attack problem in practical model optimized deep learning systems by jointly considering the DNN model-reshaping (e.g. HashNet based deep compression) and the input perturbations. We first augment adversarial example generating method dedicated to the compressed DNN models by incorporating the software-based approaches and mathematical modeled DNN reshaping. We then conduct a comprehensive robustness and vulnerability analysis of deep compressed DNN models under derived adversarial attacks. A defense technique named "gradient inhibition" is further developed to ease the generating of adversarial examples thus to effectively mitigate adversarial attacks towards both software and hardware-oriented DNNs. Simulation results show that "gradient inhibition" can decrease the average success rate of adversarial attacks from 87.99% to 4.77% (from 86.74% to 4.64%) on MNIST (CIFAR-10) benchmark with marginal accuracy degradation across various DNNs.

Toward Few-step Adversarial Training from a Frequency Perspective Machine Learning

We investigate adversarial-sample generation methods from a frequency domain perspective and extend standard $l_{\infty}$ Projected Gradient Descent (PGD) to the frequency domain. The resulting method, which we call Spectral Projected Gradient Descent (SPGD), has better success rate compared to PGD during early steps of the method. Adversarially training models using SPGD achieves greater adversarial accuracy compared to PGD when holding the number of attack steps constant. The use of SPGD can, therefore, reduce the overhead of adversarial training when utilizing adversarial generation with a smaller number of steps. However, we also prove that SPGD is equivalent to a variant of the PGD ordinarily used for the $l_{\infty}$ threat model. This PGD variant omits the sign function which is ordinarily applied to the gradient. SPGD can, therefore, be performed without explicitly transforming into the frequency domain. Finally, we visualize the perturbations SPGD generates and find they use both high and low-frequency components, which suggests that removing either high-frequency components or low-frequency components is not an effective defense.

MultAV: Multiplicative Adversarial Videos Machine Learning

The majority of adversarial machine learning research focuses on additive threat models, which add adversarial perturbation to input data. On the other hand, unlike image recognition problems, only a handful of threat models have been explored in the video domain. In this paper, we propose a novel adversarial attack against video recognition models, Multiplicative Adversarial Videos (MultAV), which imposes perturbation on video data by multiplication. MultAV has different noise distributions to the additive counterparts and thus challenges the defense methods tailored to resisting additive attacks. Moreover, it can be generalized to not only Lp-norm attacks with a new adversary constraint called ratio bound, but also different types of physically realizable attacks. Experimental results show that the model adversarially trained against additive attack is less robust to MultAV.

Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN Machine Learning

We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier network to become more robust to adversarial perturbations. Furthermore, our adversarial training framework efficiently reduces overfitting and outperforms other regularization methods such as Dropout. We applied our method to supervised learning for CIFAR datasets, and experimantal results show that our method significantly lowers the generalization error of the network. To the best of our knowledge, this is the first method which uses GAN to improve supervised learning.