Dr. Qingzhong "Frank" Liu
Dr. Liu has about twenty years’ experience in cybersecurity, digital forensics, and artificial intelligence. His study has been funded by the U.S. Army, the National Institute of Justice, the National Science Foundation, and the National Institute of Health. He was the recipient of the SHSU Excellence in Scholarly and Creative Activity Award in 2017. In 2021 and 2022, he was selected by Stanford University on the world’s top 2% scientists in Artificial Intelligence and Image Processing.
- PhD, New Mexico Institute of Mining and Technology
- Deep Learning
- Artificial Intelligence
- Computer Vision
- Multimedia Forensics
- Digital Forensics
- Computation Applications
- 2022-present, Professor
- 2016-2022, Associate Professor
- 2010-2016, Assistant Professor
Honors and Awards
- The recipient of the Fraud Impact Award at Great Houston, 2015
- SHSU College of Science and Engineering Technology Research Excellence Award, 2016
- SHSU Excellence in Scholarly and Creative Activity Award in 2017
- The world’s top 2% scientists in Artificial Intelligence and Image Processing, 2021 and 2022
- PI, Development and Quantitative Evaluation of Steganalysis and Digital Forgery Detection Systems, $331,056 ( 10/1/2010-8/31/2013, awarded from National Institute of Justice, Department of Justice), co-PIs: Andrew H. Sung & Peter A. Cooper.
- PI, CIF: SMALL: RUI: Novel Detection Approaches with Comprehensive Hybrid Intelligent Systems for Multimedia Forensics, $249,997 ( 1/1/2014-12/31/2017, awarded from National Science Foundation), co-PI: Zhongxue Chen.
- Co-PI, Analyzing No Syndromic Orofacial CleftsGWAS Data with Case Parent Trio Design, $335,932 ( 07/01/2021-06/30/2023, awarded from National Institute of Health). PI: Zhongxue Chen (Indiana University Bloomington)
- Liu Q (2019). An improved approach to exposing JPEG seam carving under recompression. IEEE Transactions on Circuits and Systems for Video Technology 29(7): 1907-1918.
- Liu Q (2017). An approach to detecting JPEG down-recompression and seam carving forgery under recompression anti-forensics, Pattern Recognition, vol. 65, pp 33-46.
- Liu Q and Chen Z (2014). Improved approaches with calibrated neighboring joint density to steganalysis and seam-carved forgery detection in JPEG images. ACM Transactions on Intelligent Systems and Technology, 5(4), article 63.
- Liu Q, Sung AH, and Qiao M (2011). Neighboring joint density-based JPEG steganalysis, ACM Transactions on Intelligent Systems and Technology 2(2), article 16 (February 2011).
- Liu Q, Sung AH and Qiao M (2009). Temporal derivative-based spectrum and mel-sepstrum audio steganalysis, IEEE Transactions on Information Forensics and Security, 4(3): 359-368.