arXiv subjects

Yuezun Li

Publications and source records attributed to Yuezun Li.

43 records · Page 3Linked to original sources

arXiv2019

Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches

Recent works succeeded to generate adversarial perturbations on the entire image or the object of interests to corrupt CNN based object detectors. In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in the background outside the object. The SSM is referred to the Region Proposal Network used in a two-stage object detector or the single-stage object detector itself. The SSM is typically a fully convolutional neural network which generates output in a single forward pass. Due to the excessive convolutions used in SSM, the actual receptive field is larger than the object itself. As such, we propose a novel method to corrupt object detectors by generating imperceptible patches only in the background. Our method can find a few background patches for perturbation, which can effectively decrease true positives and dramatically increase false positives. Efficacy is demonstrated on 5 two-stage object detectors and 8 single-stage object detectors on the MS COCO 2014 dataset. Results indicate that perturbations with small distortions outside the bounding box of object region can still severely damage the detection performance.

cs.CV↗

arXiv2019

Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations

Recent years have seen fast development in synthesizing realistic human faces using AI technologies. Such fake faces can be weaponized to cause negative personal and social impact. In this work, we develop technologies to defend individuals from becoming victims of recent AI synthesized fake videos by sabotaging would-be training data. This is achieved by disrupting deep neural network (DNN) based face detection method with specially designed imperceptible adversarial perturbations to reduce the quality of the detected faces. We describe attacking schemes under white-box, gray-box and black-box settings, each with decreasing information about the DNN based face detectors. We empirically show the effectiveness of our methods in disrupting state-of-the-art DNN based face detectors on several datasets.

cs.CV↗

arXiv2019

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 {\em 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 (CNNs). Compared to previous methods which use a large amount of real and DeepFake generated images to train CNN classifier, our method does not need DeepFake generated images as negative training examples since we target the artifacts in affine face warping as the distinctive feature to distinguish real and fake images. The advantages of our method are two-fold: (1) Such artifacts can be simulated directly using simple image processing operations on a image to make it as negative example. Since training a DeepFake model to generate negative examples is time-consuming and resource-demanding, our method saves a plenty of time and resources in training data collection; (2) Since such artifacts are general existed in DeepFake videos from different sources, our method is more robust compared to others. Our method is evaluated on two sets of DeepFake video datasets for its effectiveness in practice.

cs.CV↗

arXiv2019

Exposing GAN-synthesized Faces Using Landmark Locations

Generative adversary networks (GANs) have recently led to highly realistic image synthesis results. In this work, we describe a new method to expose GAN-synthesized images using the locations of the facial landmark points. Our method is based on the observations that the facial parts configuration generated by GAN models are different from those of the real faces, due to the lack of global constraints. We perform experiments demonstrating this phenomenon, and show that an SVM classifier trained using the locations of facial landmark points is sufficient to achieve good classification performance for GAN-synthesized faces.

cs.CV↗

arXiv2019

De-identification without losing faces

Training of deep learning models for computer vision requires large image or video datasets from real world. Often, in collecting such datasets, we need to protect the privacy of the people captured in the images or videos, while still preserve the useful attributes such as facial expressions. In this work, we describe a new face de-identification method that can preserve essential facial attributes in the faces while concealing the identities. Our method takes advantage of the recent advances in face attribute transfer models, while maintaining a high visual quality. Instead of changing factors of the original faces or synthesizing faces completely, our method use a trained facial attribute transfer model to map non-identity related facial attributes to the face of donors, who are a small number (usually 2 to 3) of consented subjects. Using the donors' faces ensures that the natural appearance of the synthesized faces, while ensuring the identity of the synthesized faces are changed. On the other hand, the FATM blends the donors' facial attributes to those of the original faces to diversify the appearance of the synthesized faces. Experimental results on several sets of images and videos demonstrate the effectiveness of our face de-ID algorithm.

cs.CV↗

arXiv2018

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.

cs.CV↗

arXiv2018

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.

cs.CV↗