Popular GitHub Repositories for Defending

Code for Defending Against Neural Fake News, https://rowanzellers.com/grover/

??Defending Against Deepfakes Using Adversarial Attacks on Conditional Image Translation Networks

Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs

The official code of KDD22 paper "FLDetecotor: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients"

Defending graph neural networks against adversarial attacks (NeurIPS 2020)

The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".

Source Code for 'SECurity evaluation platform FOR Speaker Recognition' released in 'Defending against Audio Adversarial Examples on Speaker Recognition Systems'

[ICML 2023] On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation

?? Novel algorithm for defending against Data Poisoning Attacks in a Federated Learning scenario

Official implementation of the paper DiffDefence: defending against adversarial attacks via diffusion models. ICIAP 2023.

exploiting and defending neural networks(神经网络攻防专栏)

Hands-on lessons for attacking and defending AI systems, starting with the OWASP Top 10 for LLM Applications.

Official code for "PubDef: Defending Against Transfer Attacks From Public Models" (ICLR 2024)

[ECML 22'] Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising

Official Implementation of Paper "NOMARO: Defending against Adversarial Attacks by NOMA-Inspired Reconstruction Operation"

The official implementation of "DataFreeShield: Defending Adversarial Attacks without Training Data" accepted in ICML 2024.

A New Context-Aware Framework for Defending Against Adversarial Attacks in Hyperspectral Image Classification (IEEE TGRS 2023)

Official webpage for the paper 'Defending against Audio Adversarial Examples on Speaker Recognition Systems'.

An implementation of ICLR 22 paper "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" in PyTorch

A stochastic input pre-processing technique based on a process of down-sampling/up-sampling using convolution and transposed convolution layers. Defending convolutional neural network against adversarial attacks.