Digital image forensics

Digital image forensics is a relatively new research field that aims to expose the origin and composition of, and the history of processing applied to digital images. Hence, the digital image forensics is expected to be of significant importance to our modern society in which the digital media are getting more and more popular. In this thesis, image tampering detection and classification of double JPEG compression are the two major subjects studied. Since any manipulation applied to digital images changes image statistics, identifying statistical artifacts becomes critically important in image forensics. In this thesis, a few typical forensic techniques have been studied. Finally, it is foreseen that the investigations on endless confliction between forensics and anti-forensics are to deepen our understanding on image statistics and advance civilization of our society

Application of Benford's law in deepfake image detection

Разработка и совершенствование технологий обнаружения deepfake являются одним из приоритетных направлений обеспечения социальной и биометрической безопасности. В работе исследуются перспективы применения закона Бенфорда как инструмента обнаружения deepfake-изображений, сгенерированных нейросетями GAN. Предлагаемый подход основан на анализе спектра мощности и энтропии изобра-жений. Эффективность предложенного метода апробировалась на датасетах, сгенерированных нейросетями StyleGAN2 и StyleGAN3. Предложенный метод не требует больших вычислительных мощностей

A study of probability distributions of DCT coefficients in JPEG compression

The Discrete Cosine Transform (DCT) used in JPEG compression has shown excellent energy compaction properties that rival that of the ideal Karhunen-Loève Transform. Lossy compression in JPEG is achieved by distorting 8x8 block DCT coefficients through quantization. It has been shown in literature that DC block DCT coefficients are Gaussian probability distributed and AC block DCT coefficients are Generalized Normal probability distributed. In this investigation, three probability density models for individual modes of non- quantized AC block DCT coefficients are evaluated and are used as basis for the derivation of probability distributions for quantized block DCT coefficients. The suitability of each of the three derived models is evaluated using the Kolmogorov-Smirnov and χ2 goodness-of-fit tests, and the moments of the best-fit model are derived. The best-fit model is applied to detect the presence and extent of JPEG compression history in bitmap images. A model for all quantized AC block DCT coefficients is derived using mixtures of individual quantized block DCT modes, and the model hence developed is used to validate the Generalized Benford\u27s Law for leading digit distributions of quantized AC block DCT coefficients

Закон Бенфорда и атрибуция текстов

Исследовано распределение первой значащей цифры в числительных связных текстов. Обнаружено, что закон Бенфорда приближённо выполняется для них. Отклонения от закона Бенфорда являются статистически устойчивыми авторскими особенностями, позволяющими при некоторых условиях различить части текста с разным авторством.The distribution of the first significant digit in numerals of connected texts is considered. Benford's law is found to hold approximately for them. Deviations from Benford's law are statistically significant author peculiarities that allow, under certain conditions, to distinguish between parts of the text with a different authorship

A NEW IMAGE STEGANOGRAPHY ALGORITHM BASED ON MLSB METHOD WITH RANDOM PIXELS SELECTION

ABSTRAC

A forensics software toolkit for DNA steganalysis.

Recent advances in genetic engineering have allowed the insertion of artificial DNA strands into the living cells of organisms. Several methods have been developed to insert information into a DNA sequence for the purpose of data storage, watermarking, or communication of secret messages. The ability to detect, extract, and decode messages from DNA is important for forensic data collection and for data security. We have developed a software toolkit that is able to detect the presence of a hidden message within a DNA sequence, extract that message, and then decode it. The toolkit is able to detect, extract, and decode messages that have been encoded with a variety of different coding schemes. The goal of this project is to enable our software toolkit to determine with which coding scheme a message has been encoded in DNA and then to decode it. The software package is able to decode messages that have been encoded with every variation of most of the coding schemes described in this document. The software toolkit has two different options for decoding that can be selected by the user. The first is a frequency analysis approach that is very commonly used in cryptanalysis. This approach is very fast, but is unable to decode messages shorter than 200 words accurately. The second option is using a Genetic Algorithm (GA) in combination with a Wisdom of Artificial Crowds (WoAC) technique. This approach is very time consuming, but can decode shorter messages with much higher accuracy