https://github.com/Muzammal-Naseer/NRP
Local Gradients Smoothing: Defense against localized adversarial attacks
Papers with Code 아카이브
Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.
Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Muzammal-Naseer/On-Improving-Adversarial-Transferability-of-Vision-Transformers
On Improving Adversarial Transferability of Vision Transformers
https://github.com/NJUNLP/ReNeLLM
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/Nathangitlab/Backdoor-Attacks-on-Crowd-Counting
https://github.com/NetoPedro/Universal-Adversarial-Perturbations-Pytorch
DeepFool: a simple and accurate method to fool deep neural networks
https://github.com/NeuralNetworkVerification/Marabou
Efficient Neural Network Analysis with Sum-of-Infeasibilities
https://github.com/OUC-VAS/FacePoison
Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection
https://github.com/OwenSec/DeepDetector
https://github.com/P2333/Max-Mahalanobis-Training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/PerryXDeng/adversarial_mnist_attacks
https://github.com/Ping-C/certifiedpatchdefense
Local Gradients Smoothing: Defense against localized adversarial attacks
https://github.com/QData/TextAttack/blob/master/docs/2notebook/Example_6_Chinese_Attack.ipynb
Expanding Scope: Adapting English Adversarial Attacks to Chinese
https://github.com/Rakshith-Manandi/text-to-image-using-GAN
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
https://github.com/SafiyaJan/Attacking-Neural-Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/SaoYan/LearnToPayAttention
https://github.com/Sayak007/Text-to-Image-Synthesis-using-DCGAN
https://github.com/SifatMd/Research-Papers
https://github.com/SullyChen/Autopilot-TensorFlow
https://github.com/TencentYoutuResearch/Adv-Makeup
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
https://github.com/TheLaoLab/UIBDiffusion
UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion Models
https://github.com/TonyYaoMSU/ProjectedGradientDescent
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/TonyYaoMSU/TRADES
Theoretically Principled Trade-off between Robustness and Accuracy
https://github.com/TrustAI/DeepConcolic
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/art/attacks/evasion/elastic_net.py
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
https://github.com/Trustworthy-AI-Group/TransferAttack
https://github.com/UnofficialJuliaMirrorSnapshots/MIPVerify.jl-e5e5f8be-2a6a-5994-adbb-5afbd0e30425
Provable defenses against adversarial examples via the convex outer adversarial polytope
https://github.com/Verified-Intelligence/alpha-beta-CROWN_vnncomp2024
https://github.com/Vigneshthanga/stackGAN-v2
https://github.com/VishaalMK/VectorDefense
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Vishal-V/StackGAN
https://github.com/Wickstrom/TimeSeriesXAI
https://github.com/XMUDM/ShillingAttack
https://github.com/XingLiangLondon/Image-Similarity-in-Percentage
https://github.com/YinDFY/PGD_for_targeted_attack
Towards Deep Learning Models Resistant to Adversarial Attacks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.