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Found 9 papers, 2 papers with code
Can deepfakes be created by novice users?
We conclude that creating Deepfakes is a simple enough task for a novice user given adequate tools and time; however, the resulting Deepfakes are not sufficiently real-looking and are unable to completely fool detection software as well as human examiners
Adversarial Token Attacks on Vision Transformers
Vision transformers rely on a patch token based self attention mechanism, in contrast to convolutional networks.
Provable Compressed Sensing with Generative Priors via Langevin Dynamics
Deep generative models have emerged as a powerful class of priors for signals in various inverse problems such as compressed sensing, phase retrieval and super-resolution.
Adversarially Robust Learning via Entropic Regularization
In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks.
Phase Retrieval using Untrained Neural Network Priors
Untrained deep neural networks as image priors have been recently introduced for linear inverse imaging problems such as denoising, super-resolution, inpainting and compressive sensing with promising performance gains over hand-crafted image priors such as sparsity.
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
Specifically, we consider the problem of solving linear inverse problems, such as compressive sensing, as well as non-linear problems, such as compressive phase retrieval.
Learning ReLU Networks via Alternating Minimization
We propose and analyze a new family of algorithms for training neural networks with ReLU activations.
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval
For this problem, we design a recovery algorithm that we call Block CoPRAM that further reduces the sample complexity to O(ks log n).
Sample-Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements
For this problem, we design a recovery algorithm Block CoPRAM that further reduces the sample complexity to $O(ks\log n)$.
