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Adversarial Attack

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하위 태스크: 4
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/samchevalier/dcattack

공식 코드

Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF Infeasible

https://github.com/satyamahesh84/SIDU_XAI_CODE

공식 코드

Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method

https://github.com/savannahxu79/linkprompt

공식 코드

$\textit{LinkPrompt}$: Natural and Universal Adversarial Attacks on Prompt-based Language Models

https://github.com/sbamit/exponentiated-gradient-descent-llm-attack

공식 코드

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent

https://github.com/scenarri/SMGAA

공식 코드

Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense

https://github.com/sclbd/accelerated-lpbox-admm

공식 코드

Learning to Accelerate Approximate Methods for Solving Integer Programming via Early Fixing

https://github.com/sclbd/backdoorbench

공식 코드

BackdoorBench: A Comprehensive Benchmark of Backdoor Learning

https://github.com/sclbd/mcg-blackbox

공식 코드

Generalizable Black-Box Adversarial Attack with Meta Learning

https://github.com/sclbd/transfer_attack_rap

공식 코드

Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation

https://github.com/seaocn/link-backdoor

공식 코드

Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection

https://github.com/seaocn/motif-backdoor

공식 코드

Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs

https://github.com/sebochs/adversarial_insertions

공식 코드

Cheating Automatic Short Answer Grading: On the Adversarial Usage of Adjectives and Adverbs

https://github.com/secml-lab-vt/EvolvingThreat-DeepfakeImageDetect

공식 코드

An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape

https://github.com/secure-ai-systems-group/qu-anti-zation

공식 코드

Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes

https://github.com/sejoonoh/atr

공식 코드

Adversarial Text Rewriting for Text-aware Recommender Systems

https://github.com/sensengao/vlptransferattack

공식 코드

Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory

https://github.com/seralab/robust-and-accurate-uv-map-based-camouflage-attack

공식 코드

RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation

https://github.com/serendipity618/ba-ocad

공식 코드

Backdoor Attack against One-Class Sequential Anomaly Detection Models

https://github.com/sf-zhai/badt2i

공식 코드

Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning

https://github.com/shahariar-shibli/adversarial-attack-on-pos-tags

공식 코드

Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation

https://github.com/shahidmuneer/multimodal-nsfw-defense

공식 코드

Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale Dataset

https://github.com/shahriar0651/noisec

공식 코드

Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks

https://github.com/shangtse/robust-physical-attack

공식 코드

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd

공식 코드

CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks

https://github.com/shashankskagnihotri/cospgd

공식 코드

CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks

https://github.com/shawkui/Proactive_Defensive_Backdoor

공식 코드

Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor

https://github.com/shighghyujie/newpatch-rl

공식 코드

Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks

https://github.com/shihe98/hcb

공식 코드

Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense

https://github.com/shinke-li/pointcvar

공식 코드

Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification

https://github.com/shuaizhao95/Unlearning

공식 코드

Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation

https://github.com/shuchiwu/eminspector

공식 코드

EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection

https://github.com/shuyao95/FZooS

공식 코드

Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients

https://github.com/shymuel/diff-cleanse

공식 코드

Diff-Cleanse: Identifying and Mitigating Backdoor Attacks in Diffusion Models

https://github.com/sisl-uri/arch_backdoor_llm

공식 코드

Exploiting the Vulnerability of Large Language Models via Defense-Aware Architectural Backdoor

https://github.com/sisl-uri/noiseattack

공식 코드

NoiseAttack: An Evasive Sample-Specific Multi-Targeted Backdoor Attack Through White Gaussian Noise

https://github.com/sizhe-chen/aaa

공식 코드

Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.