[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
A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.
Differentiable Optimizers with Perturbations in Pytorch
[CVPR 2018] Tensorflow implementation of NAG : Network for Adversary Generation
[ICLR'24] Official PyTorch Implementation of ContraLSP
NPI Ephemeris Propagation Tool with Uncertainty Extrapolation
Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks
Single-Cell (Perturbation) Model Library
Universal Adversarial Audio Perturbations
Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"
[ICML'24] Official PyTorch Implementation of TimeX++
Space Engineering 3 Course Work at University of Sydney
Building a multi-label classifier from scratch and using transfer learning for the PASCAL VOC image dataset.
Adversarial Attack using a DCGAN
Code to analyze high-density EEG and concurrent EMG/EOG datastreams during balance perturbations (replicates results from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088363/)
Dark photon conversions in our inhomogeneous Universe. Code repository associated with the papers https://arxiv.org/abs/2002.05165 and https://arxiv.org/abs/2004.06733.
A deep convolutional neural network is used to explain the results of another one (VGG19).
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.
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