157 citations · 159 across the 2 of their papers we have counts for
6 papers
Adversarial Threats to DeepFake Detection: A Practical Perspective
Paarth Neekhara, Brian Dolhansky, Joanna Bitton +1
Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the…
Adversarial collision attacks on image hashing functions
Brian Dolhansky, Cristian Canton Ferrer
Hashing images with a perceptual algorithm is a common approach to solving duplicate image detection problems. However, perceptual image hashing algorithms are differentiable, and…
The DeepFake Detection Challenge (DFDC) Dataset
Brian Dolhansky, Joanna Bitton, Ben Pflaum +4
Deepfakes are a recent off-the-shelf manipulation technique that allows anyone to swap two identities in a single video. In addition to Deepfakes, a variety of GAN-based face swapp…
Deep Poisoning: Towards Robust Image Data Sharing against Visual Disclosure
Hao Guo, Brian Dolhansky, Eric Hsin +3
Due to respectively limited training data, different entities addressing the same vision task based on certain sensitive images may not train a robust deep network. This paper intr…
The Deepfake Detection Challenge (DFDC) Preview Dataset
Brian Dolhansky, Russ Howes, Ben Pflaum +2
In this paper, we introduce a preview of the Deepfakes Detection Challenge (DFDC) dataset consisting of 5K videos featuring two facial modification algorithms. A data collection ca…
Eye In-Painting with Exemplar Generative Adversarial Networks
Brian Dolhansky, Cristian Canton Ferrer
This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs).…