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EZClone: Improving DNN Model Extraction Attack via Shape Distillation from GPU Execution Profiles
Prior work has shown that, once a DNN has been successfully cloned, further attacks such as model evasion or model inversion can be accelerated significantly.
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning
The prevalence and success of Deep Neural Network (DNN) applications in recent years have motivated research on DNN compression, such as pruning and quantization.
MILR: Mathematically Induced Layer Recovery for Plaintext Space Error Correction of CNNs
The increased use of Convolutional Neural Networks (CNN) in mission critical systems has increased the need for robust and resilient networks in the face of both naturally occurring faults as well as security attacks.
Deep-Lock: Secure Authorization for Deep Neural Networks
Trained Deep Neural Network (DNN) models are considered valuable Intellectual Properties (IP) in several business models.
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