Papers with Code 아카이브
Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 3
- 보존된 평가 기록
- 9
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/mj-jang/eknowia
공식 코드https://github.com/mlvccn/bmtc_transferattackvid
공식 코드https://github.com/mmoradi-iut/robustbionlp
공식 코드Improving the robustness and accuracy of biomedical language models through adversarial training
https://github.com/mobile-intelligence-lab/amoeba
공식 코드Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning
https://github.com/momalab/ReVeil
공식 코드ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
https://github.com/mostafa-ja/sigma-binary
공식 코드Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems
https://github.com/mr9812/joint-cluster-supervised-learning
공식 코드Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
https://github.com/msingh27/LAT_adversarial_robustness
공식 코드Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
https://github.com/msu-video-group/adversarial-defenses-for-iqa
공식 코드https://github.com/mti-lab/HF-ADB
공식 코드High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image Synthesis
https://github.com/mxzheng/trojvit
공식 코드https://github.com/myniuuu/advnir
공식 코드https://github.com/najeebjebreel/dbalfa
공식 코드Defending Against Backdoor Attacks by Layer-wise Feature Analysis
https://github.com/nanboy-ronan/backdoor-fedgan
공식 코드Backdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis
https://github.com/narenmanoj/mnist-adv-training
공식 코드https://github.com/natanielruiz/disrupting-deepfakes
공식 코드https://github.com/naufalso/adversarial-manhole
공식 코드https://github.com/ndb796/latenthsja
공식 코드Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries
https://github.com/ndraeger/waba
공식 코드Backdoor Attacks for Remote Sensing Data with Wavelet Transform
https://github.com/necla-ml/diff-jpeg
공식 코드https://github.com/neddamj/nicsec
공식 코드Bitstream Collisions in Neural Image Compression via Adversarial Perturbations
https://github.com/neeharperi/DeepKNNDefense
공식 코드Deep k-NN Defense against Clean-label Data Poisoning Attacks
https://github.com/nehemya/Algo-Trade-Adversarial-Examples
공식 코드Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders
https://github.com/nesl/adversarial_genattack
공식 코드GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
https://github.com/nguyenhongson1902/venomancer
공식 코드Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated Learning
https://github.com/nii-yamagishilab/fashion_adv
공식 코드https://github.com/nikfam/accelat
공식 코드https://github.com/nishadsinghi/cleanclip
공식 코드CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
https://github.com/nishiwen1214/dropattack
공식 코드DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks
https://github.com/nlsde-safety-team/fingersafe
공식 코드Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection
https://github.com/norahalshahrani/bert_synonym_attack
공식 코드Arabic Synonym BERT-based Adversarial Examples for Text Classification
https://github.com/nowindbutrain/impure
공식 코드https://github.com/ocatak/6g-channel-estimation-dataset
공식 코드https://github.com/omidmnezami/pick-object-attack
공식 코드Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection
https://github.com/optml-group/bibaddiff
공식 코드From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.