2019/06/17 by Marcus Comiter, Comiter, Marcus, Surat Teerapittayanon +3
Computer Science · Mathematics · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Security and Verification in Computing #cs.CR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1906.07148
arxiv created 2019/06/17 · openalex publication_date 2019/06/17 · arxiv updated 2019/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce CheckNet, a method for secure inference with deep neural networks on untrusted devices. CheckNet is like a checksum for neural network inference: it verifies the integrity of the inference computation performed by untrusted devices to 1) ensure the inference has actually been performed, and 2) ensure the inference has not been manipulated by an attacker. CheckNet is completely transparent to the third party running the computation, applicable to all types of neural networks, does not require specialized hardware, adds little overhead, and has negligible impact on model performance. CheckNet can be configured to provide different levels of security depending on application needs and compute/communication budgets. We present both empirical and theoretical validation of CheckNet on multiple popular deep neural network models, showing excellent attack detection (0.88-0.99 AUC) and attack success bounds.