Shu-Wei Liang

dblp:439/0721 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Digital forensics and information hiding · 100%
Artificial intelligence
1 paper
Image recognition and object detection · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Digital forensics and information hiding › steganography › image steganography
coverless image steganography
1.012026
A Coverless Image Steganography Technique Based on Multi-Object Mapping Rules · IEEE Trans. Multim. 2026
Digital forensics and information hiding
steganography
1.012026
A Coverless Image Steganography Technique Based on Multi-Object Mapping Rules · IEEE Trans. Multim. 2026
Computer vision › Image recognition and object detection › object detection
multi-object detection
0.312026
A Coverless Image Steganography Technique Based on Multi-Object Mapping Rules · IEEE Trans. Multim. 2026

Methods — techniques the papers use, named apart from their topics

multi-object detection · 2.0mapping rules · 1.0mapping rule · 1.0
YearPublicationVenuePosition
2026 A Coverless Image Steganography Technique Based on Multi-Object Mapping Rules
abstract
With the rapid development of multimedia technology and computer networks, confidential and sensitive digital information is frequently transmitted over the Internet. To pre vent this information from being intentionally damaged or forged, ensuring its security during transmission has become an important research topic. Coverless image steganography can hide secret messages within images for transmission without leaving any traces, making it one of the main methods considered for securely conveying secret information. However, existing cover less image steganography techniques overlook the possibility that the number of images in the dataset may be insufficient to con struct a complete image sequence index, rendering them impractical for data hiding applications. In light of this, this paper pro poses a novel coverless image steganography technique based on multi-object detection. By creating indexes based primarily on object labels contained within images, it establishes a flexible mapping relationship between secret message segments and im age feature sequences, thereby reducing the number of images required to construct a complete sequence index. Experimental results demonstrate that this method can effectively perform the task of hiding secret messages in real image datasets. Further-more, our method significantly outperforms previous research in terms of steganographic capacity and robustness, showing substantial improvements of 27.65% and 16.07%, respectively.
Chen-Yi Lin, Shu-Wei Liang
IEEE Trans. Multim.2