Hyoung-Joong Kim

Digital-Forensics and Watermarking

Statistical feature-based pattern recognition approach has been proved to be useful in digital image forgery detection. In this paper, we first present this approach adopted in Phase 1 of the first IEEE IFS-TC Image Forensics Challenge, in which the task is to classify the tampered images from the original ones, together with the experimental results. Several different kinds of statistical features and their combinations have been tested. Finally, we have chosen to use co-occurrence matrices calculated from the rich model of noise residual images. Furthermore we have selected a subset of the rich model to further enhance the testing accuracy by about 2 % so as to reach a high detection accuracy of 93.7 %. In Phase 2 of the competition, the task is to localize the tampered regions by identifying the tampered pixels. For this purpose, we have introduced the Hamming distance of Local Binary Patterns as similarity measure to tackle the tampering without post-processing on copy-moved regions. The PatchMatch algorithm has been adopted as an efficient search algorithm for block-matching. We have also applied a simple usage of the scale-invariant feature transform (SIFT) when other kinds of processing such as rotation and scaling have been performed on the copy-moved region. The achieved f-score in identifying tampered pixels is 0.267. In summary, some success has been achieved apparently. However, much more efforts are called for to move image tampering detection ahead.

Digital Forensics and Watermaking

In this paper, we propose a new channel selection criterion for spatial domain steganography. Via choosing those pixels in edge regions that may introduce minimal detectable distortion for modification, the proposed channel selection criterion can improve the security performance of the widespread block codes based matrix embedding schemes. Various experimental results tested on 5,000 grayscale images demonstrate the efficiency of our new channel selection criterion.Steganography and steganalysis.- Watermarking and copyright protection.- Forensics and anti-forensics.- Reversible data hiding fingerprinting and authentication.- Visual cryptography.

Ternary data hiding technique for JPEG steganography

In this paper we present JPEG steganography method based on hiding data to the stream of ternary coefficients. In the proposed method each nonzero DCT coefficient is converted to the corresponding ternary coefficient. The block of 3m - 1 ternary coefficients is used for hiding m ternary messages by modifying one or two coefficients. Due to higher information density of the ternary coefficients, the proposed method has many solutions for hiding necessary data. Such a big choice enables to choose coefficients with lowest distortion impact. As a result, the proposed methods have better data hiding performance compared to the existing steganographic methods based on hiding data to stream of binary coefficients like matrix encoding (F5) and modified matrix encoding (MME). The proposed methods were tested with steganalysis method proposed by T. Pevny and J. Fridrich. The experimental results show that the proposed method has less detectability compared to MME (modified matrix encoding).

Reversible watermarking using edge based difference modification

Reversible watermarking can embed data into the cover image and extract data from stego image, where the original cover image can be recovered perfectly after the extraction of data. Difference expansion (DE) and prediction error expansion (PEE) are two popular reversible watermarking methods. DE has the advantage of small distortion while PEE has the advantage of large embedding capacity and smaller prediction error compared with pixel difference. In this paper, we proposed a novel method that combines the advantages of DE and PEE, where the difference calculated between two pixels is combined with the edge information near this pixel pair. The proposed difference calculation can produce smaller pixel difference compared with the original simple pixel difference calculation. Overlapping embedding is then used to increase the embedding capacity. Our proposed method gives excellent results which is shown by several experiments.