https://github.com/advcolor123/advcolor
공식 코드Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
Papers with Code 아카이브
Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/adverml/multilid
공식 코드Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection
https://github.com/adverml/spectraldef_framework
공식 코드Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?
https://github.com/adversarial-for-goodness/APF
공식 코드https://github.com/adversarial-for-goodness/Co-Attack
공식 코드Towards Adversarial Attack on Vision-Language Pre-training Models
https://github.com/agencyenterprise/promptinject
공식 코드Ignore Previous Prompt: Attack Techniques For Language Models
https://github.com/agiresearch/asb
공식 코드Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
https://github.com/ai-secure/semattack
공식 코드SemAttack: Natural Textual Attacks via Different Semantic Spaces
https://github.com/aiiu-lab/backdoorimageediting
공식 코드Invisible Backdoor Triggers in Image Editing Model via Deep Watermarking
https://github.com/aika-wsd/KGPA
공식 코드KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs
https://github.com/airlabkhu/decision-badge
공식 코드Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation
https://github.com/aisecure/AdvCodec
공식 코드T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
https://github.com/ajhamdi/AdvPC
공식 코드AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds
https://github.com/ajhamdi/SADA
공식 코드SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications
https://github.com/ajhamdi/semantic-robustness
공식 코드Towards Analyzing Semantic Robustness of Deep Neural Networks
https://github.com/akil003/bert-attack
공식 코드Breaking BERT: Gradient Attack on Twitter Sentiment Analysis for Targeted Misclassification
https://github.com/akshay107/mimic-and-fool
공식 코드https://github.com/akshitjindal1/aot_wacv
공식 코드Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection
https://github.com/akuzina/defend_vae_mcmc
공식 코드Alleviating Adversarial Attacks on Variational Autoencoders with MCMC
https://github.com/albertomarchisio/DVS-Attacks
공식 코드DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks
https://github.com/alekseevskaia/orderbkd
공식 코드https://github.com/alexklwong/stereopagnosia
공식 코드Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations
https://github.com/alexklwong/targeted-adversarial-perturbations-monocular-depth
공식 코드Targeted Adversarial Perturbations for Monocular Depth Prediction
https://github.com/alibaba/federatedscope
공식 코드Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
https://github.com/aliborji/Backdoor_defense
공식 코드https://github.com/aliborji/logits
공식 코드https://github.com/alirezaabdollahpour/superdeepfool
공식 코드https://github.com/alonzolfi/adversarialmask
공식 코드Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model
https://github.com/aminbigdeli/empra
공식 코드EMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models
https://github.com/amirbelder/Random-Walks-for-Adversarial-Meshes
공식 코드https://github.com/amirgholami/trattack
공식 코드https://github.com/amunozo/parsing_perturbations
공식 코드Another Dead End for Morphological Tags? Perturbed Inputs and Parsing
https://github.com/amy-choi/AttackDefenseYOLO
공식 코드Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios
https://github.com/andreichertkov/tranfighterpro
공식 코드Translate your gibberish: black-box adversarial attack on machine translation systems
https://github.com/andreiilie1/BBAttacks
공식 코드EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks
https://github.com/anirudh9784/Adversarial-Attacks-and-Defences
공식 코드An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.