What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Jiwu Huang focuses on Artificial intelligence, Computer vision, Digital watermarking, Watermark and Pattern recognition.
His study in Artificial intelligence focuses on JPEG, Image, Pixel, Digital image and Embedding.
Jiwu Huang interconnects Composite image filter, Quantization, Lossless JPEG and Standard test image in the investigation of issues within JPEG.
His works in Transform coding, Discrete cosine transform, Image processing, Data compression and Steganography are all subjects of inquiry into Computer vision.
He combines subjects such as Audio signal processing, Multimedia, Histogram, Algorithm and Robustness with his study of Digital watermarking.
His Watermark research is multidisciplinary, incorporating elements of Affine transformation, Adaptive filter, Wavelet and Signal processing.
His most cited work include:
- Edge Adaptive Image Steganography Based on LSB Matching Revisited (462 citations)
- A Survey on Image Steganography and Steganalysis (299 citations)
- A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression (280 citations)
What are the main themes of his work throughout his whole career to date?
Jiwu Huang mainly investigates Artificial intelligence, Computer vision, Digital watermarking, Pattern recognition and Algorithm.
Steganography, Steganalysis, JPEG, Image and Feature extraction are among the areas of Artificial intelligence where Jiwu Huang concentrates his study.
His Steganography study combines topics from a wide range of disciplines, such as Pixel, Data mining and Histogram.
The various areas that Jiwu Huang examines in his Digital watermarking study include Watermark, Theoretical computer science, Signal processing, Wavelet and Robustness.
His study looks at the intersection of Pattern recognition and topics like Feature with Feature detection.
His research in Algorithm intersects with topics in Information hiding, Speech recognition and Filter.
He most often published in these fields:
- Artificial intelligence (65.15%)
- Computer vision (42.02%)
- Digital watermarking (31.92%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (65.15%)
- Pattern recognition (29.32%)
- Steganography (22.15%)
In recent papers he was focusing on the following fields of study:
Jiwu Huang mainly focuses on Artificial intelligence, Pattern recognition, Steganography, Steganalysis and Embedding.
His study focuses on the intersection of Artificial intelligence and fields such as Computer vision with connections in the field of Plaintext.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Identification, Face, Color image, Histogram and Kernel.
The study incorporates disciplines such as JPEG, Machine learning, Reinforcement learning and Data mining in addition to Steganography.
His work focuses on many connections between JPEG and other disciplines, such as Discrete cosine transform, that overlap with his field of interest in Digital audio and Signal processing.
His Embedding course of study focuses on Pixel and Algorithm, Code and Binary image.
Between 2018 and 2021, his most popular works were:
- Identification of deep network generated images using disparities in color components (68 citations)
- CNN-Based Adversarial Embedding for Image Steganography (56 citations)
- An Embedding Cost Learning Framework Using GAN (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Jiwu Huang mostly deals with Artificial intelligence, Pattern recognition, Feature extraction, Steganalysis and Embedding.
In his research on the topic of Artificial intelligence, Cloud storage is strongly related with Computer vision.
His research integrates issues of Kernel, Color image and Identification in his study of Pattern recognition.
His Feature extraction research integrates issues from Deep learning, Face and Spoofing attack.
In his study, Adversarial machine learning is strongly linked to Convolutional neural network, which falls under the umbrella field of Steganalysis.
His Embedding research incorporates themes from Algorithm and Machine learning, Reinforcement learning.
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