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08/07/2023
APBench: A Unified Benchmark for Availability Poisoning Attacks and Defenses
The efficacy of availability poisoning, a method of poisoning data by in...
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03/27/2023
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Unlearnable example attacks are data poisoning techniques that can be us...
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12/20/2022
Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentation
Open software supply chain attacks, once successful, can exact heavy cos...
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11/15/2022
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack
Adversarial attacks can deceive neural networks by adding tiny perturbat...
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10/05/2022
Revisiting Structured Dropout
Large neural networks are often overparameterised and prone to overfitti...
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10/15/2021
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial e...
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09/10/2021
Rapid Model Architecture Adaption for Meta-Learning
Network Architecture Search (NAS) methods have recently gathered much at...
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04/19/2021
LAFEAT: Piercing Through Adversarial Defenses with Latent Features
Deep convolutional neural networks are susceptible to adversarial attack...
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03/21/2020
Probabilistic Dual Network Architecture Search on Graphs
We present the first differentiable Network Architecture Search (NAS) fo...
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10/21/2019
Automatic Generation of Multi-precision Multi-arithmetic CNN Accelerators for FPGAs
Modern deep Convolutional Neural Networks (CNNs) are computationally dem...
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09/06/2019
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information
Recent research on reinforcement learning has shown that trained agents ...
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03/07/2019
Efficient and Effective Quantization for Sparse DNNs
Deep convolutional neural networks (CNNs) are powerful tools for a wide ...
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01/23/2019
Sitatapatra: Blocking the Transfer of Adversarial Samples
Convolutional Neural Networks (CNNs) are widely used to solve classifica...
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10/12/2018
