{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/design-and-analysis-of-novel-bit-flip-attacks","title":"Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs","arxiv_id":null,"date":"2022-06-24","proceeding":"IEEE Conference on Dependable and Secure Computing (DSC) 2022 6","authors":["Yash Khare","Kumud Lakara","Maruthi S Inukonda","Sparsh Mittal","Mahesh Chandra","Arvind Kaushik"],"abstract":"The security of deep neural networks (DNNs) has become a matter of grave concern in the past few years due to their increasing ubiquity in security-critical domains. In this paper, we present novel bit-flip attack (BFA) algorithms for DNNs, along with techniques for defending against the attack. Our attack algorithms leverage information about the layer importance, such that a layer is considered important if it has high-ranked feature maps.  We first present a  classwise-targeted attack that degrades the accuracy of just one class in the dataset. Comparative evaluation with related works shows the effectiveness of our attack algorithm.  We finally propose multiple novel defense strategies against untargeted BFAs. We comprehensively evaluate the robustness of both large-scale CNNs (VGG19, ResNext50, AlexNet and ResNet) and compact CNNs (MobileNet-v2, ShuffleNet, GoogleNet and SqueezeNet) towards BFAs. We also reveal a valuable insight that compact CNNs are highly vulnerable to not only well-crafted BFAs such as ours, but even random BFAs. Also, defense strategies are less effective on compact CNNs. This fact makes them unsuitable for use in security-critical domains.","url_abs":"https://www.researchgate.net/publication/355116625_Design_and_Analysis_of_Novel_Bit-flip_Attacks_and_Defense_Strategies_for_DNNs","url_pdf":"https://www.researchgate.net/publication/355116625_Design_and_Analysis_of_Novel_Bit-flip_Attacks_and_Defense_Strategies_for_DNNs","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"design-and-analysis-of-novel-bit-flip-attacks","repo_url":"https://github.com/yashk2000/BFA-Attacks-and-Defences","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"channel-shuffle","method_name":"Channel Shuffle"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"groupwise-point-convolution","method_name":"Groupwise Point Convolution"},{"method_slug":"inception-module","method_name":"Inception Module"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"shufflenet","method_name":"ShuffleNet"},{"method_slug":"shufflenet-block","method_name":"ShuffleNet Block"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}