arXiv:2209.06113
공식 코드 · Papers with Code
Generate synthetic samples from tabular data
원문 초록
Generating new samples from data sets can mitigate extra expensive operations, increased invasive procedures, and mitigate privacy issues. These novel samples that are statistically robust can be used as a temporary and intermediate replacement when privacy is a concern. This method can enable better data sharing practices without problems relating to identification issues or biases that are flaws for an adversarial attack.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
VIP: Visual Information Protection through Adversarial Attacks on Vision-Language Models
Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF Infeasible
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability
토론
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