Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search
arXiv:1903.12269
Abstract
Several important security issues of Deep Neural Network (DNN) have been raised recently associated with different applications and components. The most widely investigated security concern of DNN is from its malicious input, a.k.a adversarial example. Nevertheless, the security challenge of DNN's parameters is not well explored yet. In this work, we are the first to propose a novel DNN weight attack methodology called Bit-Flip Attack (BFA) which can crush a neural network through maliciously flipping extremely small amount of bits within its weight storage memory system (i.e., DRAM). The bit-flip operations could be conducted through well-known Row-Hammer attack, while our main contribution is to develop an algorithm to identify the most vulnerable bits of DNN weight parameters (stored in memory as binary bits), that could maximize the accuracy degradation with a minimum number of bit-flips. Our proposed BFA utilizes a Progressive Bit Search (PBS) method which combines gradient ranking and progressive search to identify the most vulnerable bit to be flipped. With the aid of PBS, we can successfully attack a ResNet-18 fully malfunction (i.e., top-1 accuracy degrade from 69.8% to 0.1%) only through 13 bit-flips out of 93 million bits, while randomly flipping 100 bits merely degrades the accuracy by less than 1%.
References in corpus (9)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
- ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
- DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
- Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
- Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US
- Hardware Trojan Attacks on Neural Networks
- DeepLaser: Practical Fault Attack on Deep Neural Networks