| DA3: Dynamic Additive Attention Adaption for Memory-Efficient On-Device Multi-Domain Learning | 0 | 0.34 | 2022 |
| DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories | 0 | 0.34 | 2022 |
| Improving DNN Hardware Accuracy by In-Memory Computing Noise Injection | 0 | 0.34 | 2022 |
| ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | 0 | 0.34 | 2022 |
| RepNet: Efficient On-Device Learning via Feature Reprogramming. | 0 | 0.34 | 2022 |
| T-BFA: <underline>T</underline>argeted <underline>B</underline>it-<underline>F</underline>lip Adversarial Weight <underline>A</underline>ttack | 2 | 0.41 | 2022 |
| Robust Machine Learning Via Privacy/Rate-Distortion Theory | 0 | 0.34 | 2021 |
| NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing | 0 | 0.34 | 2021 |
| Defending Bit-Flip Attack Through Dnn Weight Reconstruction | 0 | 0.34 | 2020 |
| Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack | 0 | 0.34 | 2020 |
| TBT: Targeted Neural Network Attack with Bit Trojan | 0 | 0.34 | 2020 |
| Sparse BD-Net: A Multiplication-less DNN with Sparse Binarized Depth-wise Separable Convolution | 4 | 0.37 | 2020 |
| Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search | 4 | 0.41 | 2019 |
| Bit-Flip Attack: Crushing Neural Network withProgressive Bit Search. | 0 | 0.34 | 2019 |
| Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack | 4 | 0.41 | 2019 |
| Defense-Net - Defend Against a Wide Range of Adversarial Attacks through Adversarial Detector. | 1 | 0.35 | 2019 |
| Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness. | 0 | 0.34 | 2019 |
| Defend Deep Neural Networks Against Adversarial Examples via Fixed andDynamic Quantized Activation Functions. | 3 | 0.37 | 2018 |
| PIM-TGAN: A Processing-in-Memory Accelerator for Ternary Generative Adversarial Networks | 0 | 0.34 | 2018 |
| Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples. | 3 | 0.36 | 2018 |
| Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack. | 8 | 0.45 | 2018 |
| BD-NET: A Multiplication-Less DNN with Binarized Depthwise Separable Convolution | 1 | 0.36 | 2018 |
