Exposing Deep Fakes Using Inconsistent Head Poses

In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes). Our method is based on the observations that Deep Fakes are created by splicing synthesized face region into the original image, and in doing so, introducing errors that can be revealed when 3D head poses are estimated from the face images. We perform experiments to demonstrate this phenomenon and further develop a classification method based on this cue. Using features based on this cue, an SVM classifier is evaluated using a set of real face images and Deep Fakes.

Exposing DeepFake Videos By Detecting Face Warping Artifacts

In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as DeepFake videos hereafter) from real videos. Our method is based on the observations that current DeepFake algorithm can only generate images of limited resolutions, which need to be further warped to match the original faces in the source video. Such transforms leave distinctive artifacts in the resulting DeepFake videos, and we show that they can be effectively captured by convolutional neural networks. Our method is evaluated on a set of DeepFake videos for its effectiveness in practice.

Attacking Object Detectors via Imperceptible Patches on Background

Deep neural networks have been proven vulnerable against adversarial perturbations. Recent works succeeded to generate adversarial perturbations on either the entire image or on the target of interests to corrupt object detectors. In this paper, we investigate the vulnerability of object detectors from a new perspective --- adding minimal perturbations on small background patches outside of targets to fail the detection results. Our work focuses on attacking the common component in the state-of-the-art detectors (e.g. Faster R-CNN), Region Proposal Networks (RPNs). As the receptive fields generated by RPN is often larger than the proposals themselves, we propose a novel method to generate background perturbation patches, and show that the perturbations solely outside of the targets can severely damage the performance of multiple types of detectors by simultaneously decreasing the true positives and increasing the false positives. We demonstrate the efficacy of our method on 5 different state-of-the-art object detectors on MS COCO 2014 dataset.

Robust Adversarial Perturbation on Deep Proposal-based Models

Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms. In this paper, we describe a robust adversarial perturbation (R-AP) method to attack deep proposal-based object detectors and instance segmentation algorithms. Our method focuses on attacking the common component in these algorithms, namely Region Proposal Network (RPN), to universally degrade their performance in a black-box fashion. To do so, we design a loss function that combines a label loss and a novel shape loss, and optimize it with respect to image using a gradient based iterative algorithm. Evaluations are performed on the MS COCO 2014 dataset for the adversarial attacking of 6 state-of-the-art object detectors and 2 instance segmentation algorithms. Experimental results demonstrate the efficacy of the proposed method.

In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking

The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos. In this work, we describe a new method to expose fake face videos generated with neural networks. Our method is based on detection of eye blinking in the videos, which is a physiological signal that is not well presented in the synthesized fake videos. Our method is tested over benchmarks of eye-blinking detection datasets and also show promising performance on detecting videos generated with DeepFake.