What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Operating system
Artificial intelligence, Computer vision, Algorithm, Steganography and Information hiding are his primary areas of study.
His biological study deals with issues like Pattern recognition, which deal with fields such as Feature and Similarity.
Chin-Chen Chang usually deals with Computer vision and limits it to topics linked to Cryptography and Encryption, Computer network, Access control and Color image.
His Algorithm research includes elements of Scheme, Data mining and Value.
His Steganography research includes themes of Cover, Image segmentation, Theoretical computer science and Grayscale.
As a part of the same scientific family, he mostly works in the field of Information hiding, focusing on Lossless compression and, on occasion, Sobel operator.
His most cited work include:
- Reversible Data Hiding Based on Histogram Modification of Pixel Differences (444 citations)
- A steganographic method based upon JPEG and quantization table modification (320 citations)
- Finding optimal least-significant-bit substitution in image hiding by dynamic programming strategy (317 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Computer vision, Computer security and Scheme.
His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition.
His work carried out in the field of Algorithm brings together such families of science as Information hiding, Block, Data mining and Data compression ratio.
As part of one scientific family, Chin-Chen Chang deals mainly with the area of Information hiding, narrowing it down to issues related to the Steganography, and often Cover.
His Computer security study frequently draws connections to adjacent fields such as Computer network.
In his research, Secret sharing and Hash function is intimately related to Theoretical computer science, which falls under the overarching field of Scheme.
He most often published in these fields:
- Artificial intelligence (34.83%)
- Algorithm (27.49%)
- Computer vision (26.18%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (34.83%)
- Information hiding (19.46%)
- Algorithm (27.49%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Information hiding, Algorithm, Image and Scheme.
Chin-Chen Chang interconnects Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence.
His study explores the link between Computer vision and topics such as Cover that cross with problems in Secret sharing.
Chin-Chen Chang has researched Information hiding in several fields, including Image quality, Pixel, Encryption and Steganography.
His Algorithm research focuses on Block Truncation Coding and how it relates to Bitmap.
Chin-Chen Chang has included themes like Theoretical computer science, Computer security, Authentication, Code and Error detection and correction in his Scheme study.
Between 2015 and 2021, his most popular works were:
- A Provably Secure, Efficient, and Flexible Authentication Scheme for Ad hoc Wireless Sensor Networks (149 citations)
- Dynamic-Hash-Table Based Public Auditing for Secure Cloud Storage (101 citations)
- Blockchain based searchable encryption for electronic health record sharing (82 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Operating system
Chin-Chen Chang mainly focuses on Algorithm, Information hiding, Scheme, Artificial intelligence and Image.
Chin-Chen Chang combines subjects such as Theoretical computer science, Pixel, Embedding, Steganography and Matrix with his study of Algorithm.
His Information hiding research also works with subjects such as
- Image quality which intersects with area such as Discrete wavelet transform,
- Encryption which connect with Key and Data mining.
His Scheme study incorporates themes from Computer security and Authentication.
His studies deal with areas such as Payload, Host, Computer vision and Pattern recognition as well as Artificial intelligence.
His research integrates issues of Distortion, Iterative reconstruction and Permutation in his study of Image.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
