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Found 5 papers, 3 papers with code
Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples
This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform.
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches
To improve the ability of Ad-YOLO to detect variety patches, we first use an adversarial training process to develop a patch dataset based on the Inria dataset, which we name Inria-Patch.
Blind Adversarial Pruning: Balance Accuracy, Efficiency and Robustness
Furthermore, to better balance the AER, we propose an approach called blind adversarial pruning (BAP), which introduces the idea of blind adversarial training into the gradual pruning process.
Blind Adversarial Training: Balance Accuracy and Robustness
The main idea of this approach is to use a cutoff-scale strategy to adaptively estimate a nonuniform budget to modify the AEs used in the training, ensuring that the strengths of the AEs are dynamically located in a reasonable range and ultimately improving the overall robustness of the AT model.
Equivalence of restricted Boltzmann machines and tensor network states
Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures.
