Fingerprinting JPEGs With Optimised Huffman Tables

A common task in digital forensics investigations is to identify known contraband images. This is typically achieved by calculating a cryptographic digest, using hashing algorithms such as SHA256, for each image on a given medium, and comparing individual digests with a database of known contraband. However, the large capacities of modern storage media and time pressures placed on forensics examiners necessitates the development of more efficient processing methods. This work describes a technique for fingerprinting JPEGs with optimised Huffman tables which requires only the image header to be present on the media. Such fingerprints are shown to be robust across large datasets, with demonstrably faster processing times

Image Compression using Discrete Cosine Transform & Discrete Wavelet Transform

Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Compression is achieved by the removal of one or more of three basic data redundancies: (1) Coding redundancy, which is present when less than optimal (i.e. the smallest length) code words are used; (2) Interpixel redundancy, which results from correlations between the pixels of an image & (3) psycho visual redundancy which is due to data that is ignored by the human visual system (i.e. visually nonessential information). Huffman codes contain the smallest possible number of code symbols (e.g., bits) per source symbol (e.g., grey level value) subject to the constraint that the source symbols are coded one at a time. So, Huffman coding when combined with technique of reducing the image redundancies using Discrete Cosine Transform (DCT) helps in compressing the image data to a very good extent. The Discrete Cosine Transform (DCT) is an example of transform coding. The current JPEG standard uses the DCT as its basis. The DC relocates the highest energies to the upper left corner of the image. The lesser energy or information is relocated into other areas. The DCT is fast. It can be quickly calculated and is best for images with smooth edges like photos with human subjects. The DCT coefficients are all real numbers unlike the Fourier Transform. The Inverse Discrete Cosine Transform (IDCT) can be used to retrieve the image from its transform representation. The Discrete wavelet transform (DWT) has gained widespread acceptance in signal processing and image compression. Because of their inherent multi-resolution nature, wavelet-coding schemes are especially suitable for applications where scalability and tolerable degradation are important. Recently the JPEG committee has released its new image coding standard, JPEG-2000, which has been based upon DWT

Image coding for monochrome and colour images.

This work is an investigation of different algorithms to implement a lossy compression scheme. Special emphasis was focused on the quantization techniques. A CODEC (coder/decoder) based on a scheme proposed for standardization by a group known as JPEG (Joint Photographic Experts Group) was developed. Finally, a new decoding approach was developed, based on modifying the concepts of transition table used in compilers to break a binary string into variable length codes. The JPEG algorithm works in sequential mode by dividing the image into small blocks of 8 x 8 pixels. Each block is compressed separately by processing it through an 8 x 8 Discrete Cosine Transform, Quantization, Run length and Huffman coding. The two dimensional DCT was implemented by a fast 1-D DCT expanded into a 2-D DCT, using the row-column method. Quantization is carried out by dividing the transformed block by the JPEG scaling matrix and rounding the results to the nearest integer. It was found to work well for a large number of images. Four static Huffman code tables are used to convert the quantized DCT coefficients into variable length codes for both monochrome and colour images. The algorithm is capable of obtaining varying compression ratios by simply changing the scaling factor of the JPEG Quantization Matrix . The bit rate achieved was in the range of 1 bit/pixel for images indistinguishable from the original. Higher compression ratios can be obtained at the cost of lower image quality.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1993 .S465. Source: Masters Abstracts International, Volume: 32-02, page: 0691. Adviser: M. A. Sid-Ahmed. Thesis (M.A.Sc.)--University of Windsor (Canada), 1993

Utilising Reduced File Representations to Facilitate Fast Contraband Detection

Digital forensics practitioners can be tasked with analysing digital data, in all its forms, for legal proceedings. In law enforcement, this largely involves searching for contraband media, such as illegal images and videos, on a wide array of electronic devices. Unfortunately, law enforcement agencies are often under-resourced and under-staffed, while the volume of digital evidence, and number of investigations, continues to rise each year, contributing to large investigative backlogs.A primary bottleneck in forensic processing can be the speed at which data is acquired from a disk or network, which can be mitigated with data reduction techniques. The data reduction approach in this thesis uses reduced representations for individual images which can be used in lieu of cryptographic hashes for the automatic detection of illegal media. These approaches can facilitate reduced forensic processing times, faster investigation turnaround, and a reduction in the investigative backlog.Reduced file representations are achieved in two ways. The first approach is to generate signatures from partial files, where highly discriminative features are analysed, while reading as little of the file as possible. Such signatures can be generated using either header features of a particular file format, or by reading logical data blocks. This works best when reading from the end of the file. These sub-file signatures are particularly effective on solid state drives and networked drives, reducing processing times by up to 70× compared to full file cryptographic hashing. Overall the thesis shows that these signatures are highly discriminative, or unique, at the million image scale, and are thus suitable for the forensic context. This approach is effectively a starting point for developing forensics techniques which leverage the performance characteristics of non-mechanical media, allowing for evidence on flash based devices to be processed more efficiently.The second approach makes use of thumbnails, particularly those stored in the Windows thumbnail cache database. A method was developed which allows for image previews for an entire computer to be parsed in less than 20 seconds using cryptographic hashes, effecting rapid triage. The use of perceptual hashing allows for variations between operating systems to be accounted for, while also allowing for small image modifications to be captured in an analysis. This approach is not computationally expensive but has the potential to flag illegal media in seconds, rather than an hour in traditional triage, making a good starting point for investigations of illegal media

RISE: A ROBUST IMAGE SEARCH ENGINE

This thesis advances RISE (Robust Image Search Engine), an image database application designed to build and search an image repository. rise is built on the foundation of a CBIR (Content Based Image Retrieval) system. The basic goal of this system is to compute content similarity of images based on their color signatures. The color signature of an image is computed by systematically dividing the image into a number of small blocks and computing the average color of each block using ideas from DCT (Discrete Cosine Transform) that forms the basis for JPEG (Joint Photographic Experts Group) compression format. The average color extracted from each block is used to construct a tree structure and finally, the tree structure is compared with similar structures already stored in the database. During the query process, an image is given to the system as a query image and the system returns a set of images that have similar content or color distribution as the given image. The query image is processed to create its signature which is then matched against similar signature of images already stored in the database. The content similarity is measured by computing normalized Euclidean distance between the query image and the images already stored in the database. RISE has a GUI (Graphic User Interface) front end and a Java servlet in the back end that searches the images stored in the database and returns the results to the web browser. RISE enhances the performance of image operations of the system using JAI (Java Advance Imaging) tools

Bitplanes Block Based Exact Image Compression

Abstract: In this paper, an exact image compression based on bit-planes blocking is proposed. The proposed algorithm uses two bit codes for block representation. The codes represent the states of Unicode block and non-Unicode. The algorithm considers further division to non-Unicode block. The block division continues until the smallest block size which are kept as residuals. The smallest block size in the study is two by two. The main process of encoding consumed three codes. Subsequent process uses the fourth code for further compression. The resultant file is subject to further exact compression. The compression technique considered in this study is Huffman. The compression-decompression implementation complexity is comparable with the well-known methods. Also, the compression ratio for the algorithm is comparable with well-known methods. The algorithm parallelization is straightforward and dependent on number of planes. Within a plane, the process hardware realization is simple and does on require special hardware