Adversarial Defense by Stratified Convolutional Sparse Coding
arXiv:1812.00037
Abstract
We propose an adversarial defense method that achieves state-of-the-art performance among attack-agnostic adversarial defense methods while also maintaining robustness to input resolution, scale of adversarial perturbation, and scale of dataset size. Based on convolutional sparse coding, we construct a stratified low-dimensional quasi-natural image space that faithfully approximates the natural image space while also removing adversarial perturbations. We introduce a novel Sparse Transformation Layer (STL) in between the input image and the first layer of the neural network to efficiently project images into our quasi-natural image space. Our experiments show state-of-the-art performance of our method compared to other attack-agnostic adversarial defense methods in various adversarial settings.
arXiv commentPublished at CVPR 2019
References in corpus (16)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Explaining and Harnessing Adversarial Examples
- Ensemble Adversarial Training: Attacks and Defenses
- Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
- ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
- Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
- Delving into Transferable Adversarial Examples and Black-box Attacks
- Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
- Countering Adversarial Images using Input Transformations
- PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
- A study of the effect of JPG compression on adversarial images
- Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
- Convolutional Dictionary Learning: A Comparative Review and New Algorithms
- Convolutional Sparse Coding for High Dynamic Range Imaging
- Biologically inspired protection of deep networks from adversarial attacks
- Divide, Denoise, and Defend against Adversarial Attacks