University at Buffalo, State University of New York
Author pages are created from data sourced from our academic publisher partnerships and public sources.
Share This Author
The Visual Object Tracking VOT2016 Challenge Results
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
The Visual Object Tracking VOT2017 Challenge Results
- M. Kristan, A. Leonardis, +101 authors Zhiqun He
- Computer ScienceIEEE International Conference on Computer Vision…
- 22 October 2017
The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art…
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
- Yuezun Li, Xin Yang, Pu Sun, H. Qi, Siwei Lyu
- Computer Science, EngineeringIEEE/CVF Conference on Computer Vision and…
- 27 September 2019
This work presents a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process and conducts a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celebrity-DF.
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
An approach to detecting hidden messages in images that uses a wavelet-like decomposition to build higher-order statistical models of natural images and support vector machines are used to discriminate between untouched and adulterated images.
UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking
Region Duplication Detection Using Image Feature Matching
The proposed method starts by estimating the transform between matched scale invariant feature transform (SIFT) keypoints, which are insensitive to geometrical and illumination distortions, and then finds all pixels within the duplicated regions after discounting the estimated transforms.
Exposing Deep Fakes Using Inconsistent Head Poses
- Xin Yang, Yuezun Li, Siwei Lyu
- Computer ScienceICASSP - IEEE International Conference on…
- 1 November 2018
This paper proposes a new method to expose AI-generated fake face images or videos 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.
Steganalysis using higher-order image statistics
A universal approach to steganalysis for detecting the presence of hidden messages embedded within digital images, which shows that, within multiscale, multiorientation image decompositions (e.g., wavelets), first- and higher-order magnitude and phase statistics are relatively consistent across a broad range of images, but are disturbed by the existence of embedded hidden messages.
Object-Driven Text-To-Image Synthesis via Adversarial Training
- Wenbo Li, Pengchuan Zhang, +4 authors Jianfeng Gao
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 27 February 2019
A thorough comparison between the classic grid attention and the new object-driven attention is provided through analyzing their mechanisms and visualizing their attention layers, showing insights of how the proposed model generates complex scenes in high quality.
Exposing DeepFake Videos By Detecting Face Warping Artifacts
A new deep learning based method that can effectively distinguish AI-generated fake videos from real videos is described, which saves a plenty of time and resources in training data collection and is more robust compared to others.