activity
20172021
most citedEfficient video integrity analysis through container characterization

36 citations · 47 across the 3 of their papers we have counts for

collaborators

8 papers

cs.MM202136 cited

Efficient video integrity analysis through container characterization

Pengpeng Yang, Daniele Baracchi, Massimo Iuliani +4

Most video forensic techniques look for traces within the data stream that are, however, mostly ineffective when dealing with strongly compressed or low resolution videos. Recent r…

cs.MM20192 cited

Dual-Domain Fusion Convolutional Neural Network for Contrast Enhancement Forensics

Pengpeng Yang, Rongrong Ni, Yao Zhao +2

Contrast enhancement (CE) forensics techniques have always been of great interest for image forensics community, as they can be an effective tool for recovering image history and i…

cs.MM2018

Double JPEG Compression Detection by Exploring the Correlations in DCT Domain

Pengpeng Yang, Rongrong Ni, Yao Zhao

In the field of digital image processing, JPEG image compression technique has been widely applied. And numerous image processing software suppose this. It is likely for the images…

cs.CV2018

Security Consideration For Deep Learning-Based Image Forensics

Wei Zhao, Pengpeng Yang, Rongrong Ni +2

Recently, image forensics community has paied attention to the research on the design of effective algorithms based on deep learning technology and facts proved that combining the…

cs.MM2018

Robust Contrast Enhancement Forensics Using Pixel and Histogram Domain CNNs

Pengpeng Yang, Rongrong Ni, Yao Zhao +2

Contrast enhancement (CE) forensics has always been ofconcern to image forensics community. It can provide aneffective tool for recovering image history and identifyingtampered ima…

eess.IV2018

Non-Local Graph-Based Prediction For Reversible Data Hiding In Images

Qi Chang, Gene Cheung, Yao Zhao +2

Reversible data hiding (RDH) is desirable in applications where both the hidden message and the cover medium need to be recovered without loss. Among many RDH approaches is predict…