Search Results for author:
Found 8 papers, 6 papers with code
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
We present a method that allows us to train adversarially robust deep networks for tabular data and to transfer this robustness to other classifiers via universal robust embeddings tailored to categorical data.
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
We argue that, due to the differences between tabular data and images or text, existing threat models are not suitable for tabular domains.
Darker than Black-Box: Face Reconstruction from Similarity Queries
Several methods for inversion of face recognition models were recently presented, attempting to reconstruct a face from deep templates.
On the effectiveness of adversarial training against common corruptions
First, we show that, when used with an appropriately selected perturbation radius, $\ell_p$ adversarial training can serve as a strong baseline against common corruptions improving both accuracy and calibration.
[Re] Warm-Starting Neural Network Training
We reproduce the results of the paper ”On Warm-Starting Neural Network Training.” In many real-world applications, the training data is not readily available and is accumulated over time.
Black-Box Face Recovery from Identity Features
In this work, we present a novel algorithm based on an it-erative sampling of random Gaussian blobs for black-box face recovery, given only an output feature vector of deep face recognition systems.
On adversarial patches: real-world attack on ArcFace-100 face recognition system
We examine security of one of the best public face recognition systems, LResNet100E-IR with ArcFace loss, and propose a simple method to attack it in the physical world.
Real-world adversarial attack on MTCNN face detection system
Recent studies proved that deep learning approaches achieve remarkable results on face detection task.
