What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Steganography, Steganalysis and Embedding.
His works in Digital watermarking, Lossless compression, Image processing, Digital image and Grayscale are all subjects of inquiry into Artificial intelligence.
His research in Image processing intersects with topics in Image resolution, Image sensor and Image noise.
His Digital imaging study in the realm of Digital image interacts with subjects such as Least significant bit.
His work in Steganography addresses issues such as JPEG, which are connected to fields such as Histogram.
His study in Steganalysis is interdisciplinary in nature, drawing from both Pixel, Quantization and Pattern recognition.
His most cited work include:
- Digital camera identification from sensor pattern noise (878 citations)
- Determining Image Origin and Integrity Using Sensor Noise (649 citations)
- Detecting LSB steganography in color, and gray-scale images (626 citations)
What are the main themes of his work throughout his whole career to date?
Miroslav Goljan mainly investigates Artificial intelligence, Computer vision, Steganography, Steganalysis and Embedding.
Artificial intelligence is represented through his Digital image, Digital watermarking, Image processing, Digital imaging and Lossless compression research.
His Digital watermarking study integrates concerns from other disciplines, such as Watermark, Data compression, Lossy compression and Image file formats.
His studies deal with areas such as JPEG, Theoretical computer science and Algorithm, Quantization as well as Steganography.
His Steganalysis research is multidisciplinary, relying on both Pixel, Multimedia, Quantization and Pattern recognition.
His Embedding study incorporates themes from Image segmentation and Data mining.
He most often published in these fields:
- Artificial intelligence (77.46%)
- Computer vision (70.42%)
- Steganography (43.66%)
What were the highlights of his more recent work (between 2012-2020)?
- Artificial intelligence (77.46%)
- Computer vision (70.42%)
- Camera identification (5.63%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Camera identification, Estimation and Steganalysis are his primary areas of study.
His Artificial intelligence course of study focuses on Pattern recognition and Demosaicing.
He incorporates Computer vision and Distortion in his studies.
His Camera identification research incorporates themes from Pixel, Fingerprint, Matching, Camera resectioning and Compression.
The subject of his Steganalysis research is within the realm of Steganography.
His Fingerprint recognition research is multidisciplinary, incorporating perspectives in Focal length, Geometric distortion and Standard test image.
Between 2012 and 2020, his most popular works were:
- Rich model for Steganalysis of color images (62 citations)
- CFA-aware features for steganalysis of color images (32 citations)
- Effect of Compression on Sensor-Fingerprint Based Camera Identification. (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Miroslav Goljan spends much of his time researching Artificial intelligence, Computer vision, Color quantization, Color balance and Color image.
As part of his studies on Computer vision, Miroslav Goljan often connects relevant areas like Pattern recognition.
Miroslav Goljan conducts interdisciplinary study in the fields of Pattern recognition and Color filter array through his works.
The concepts of his Color quantization study are interwoven with issues in Color histogram, Color depth and High color.
His Compression research integrates issues from Fingerprint, Camera identification and Camera resectioning.
His Color space study frequently involves adjacent topics like RGB color model.
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.
