[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection
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Updated
Jul 9, 2021 - Python
[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection
Implementation of Papers on Adversarial Examples
Differentiable Optimizers with Perturbations in Pytorch
[CVPR 2018] Tensorflow implementation of NAG : Network for Adversary Generation
[ICLR'24] Official PyTorch Implementation of ContraLSP
Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks
Universal Adversarial Audio Perturbations
[ICML'24] Official PyTorch Implementation of TimeX++
Adversarial Attack using a DCGAN
In this work, we extend the FGSM method proposing multistep adversarial perturbation (MSAP) procedures to study the recommenders’ robustness under powerful methods. Letting fixed the perturbation magnitude, we illustrate that MSAP is much more harmful than FGSM in corrupting the recommendation performance of BPR-MF.
A Character-level Perturbation Generator based on probability distribution, density and diversity.
Scalable Expressiveness through Preprocessed Graph Perturbations (CIKM 2024)
DeepDefend is an open-source Python library for adversarial attacks and defenses in deep learning models, enhancing the security and robustness of AI systems.
This is the enhanced version of Stadius Move on Thionville, the traffic info scraping bot for Citéline buses.
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