We present a framework for compressing encrypted media, such as images and videos. Encryption masks the source, rendering traditional compression algorithms ineffective. By conceiving of the problem as one of distributed source coding, it has been shown in prior work that encrypted data are as compressible as unencrypted data. However, there are two major challenges to realize these theoretical results. The first is the development of models that capture the underlying statistical structure and are compatible with our framework. The second is that since the source is masked by encryption, the compressor does not know what rate to target. We tackle these issues in this paper. We first develop statistical models for images before extending it to videos, where our techniques really gain traction. As an illustration, we compare our results to a state-of-the-art motion-compensated lossless video encoder that requires unencrypted video input. The latter compresses each unencrypted frame of the ldquoForemanrdquo test sequence by 59% on average. In comparison, our proof-of-concept implementation, working on encrypted data, compresses the same sequence by 33%. Next, we develop and present an adaptive protocol for universal compression and show that it converges to the entropy rate. Finally, we demonstrate a complete implementation for encrypted video.

We consider video sequences that have been encrypted uncompressed. Since encryption masks the source, traditional data compression algorithms are rendered ineffective. However, it has been shown that through the use of distributed source-coding techniques, the compression of encrypted data is in fact possible. This means that it is possible to reduce data size without requiring that the data be compressed prior to encryption. Indeed, under some reasonable conditions, neither security nor compression efficiency need be sacrificed when compression is performed on the encrypted data (Johnson et al., 2004). In this paper we develop an algorithm for the practical lossless compression of encrypted gray scale video. Our method is based on considering the temporal correlations in the video. This move to temporal dependence builds on our previous work on memoryless sources, and one- and two-dimensional Markov sources. For comparison, a motion-compensated lossless video encoder can compress each unencrypted frame of the standard "Foreman" test video sequence by about 57%. Our algorithm can compress the same frames, after encryption, by about 33%

Coding schemes for secure and efficient communication over noiseless public channels traditionally compress and then encrypt the source data. In some cases reversing the ordering of compression and encryption would be useful, e.g., in enabling the efficient distribution of protected media content. Indeed, not only is it possible to reverse the order, but under some conditions neither security nor compression efficiency need be sacrificed. In earlier work on this problem we have assumed that the source data is either memoryless or has a 1-D Markov structure. Such models are poor matches for the 2-D structure of images. In this work, we use a 2-D source model, and develop a scheme to compress encrypted images based on LDPC codes. We present practical simulation results for compressing bi-level images. In tests, we are able to compress an encrypted 10, 000 bit bi-level image to 4, 299 bits and successfully recover the image exactly. In previous works, the best analogous 1-D model (operating on a raster scanned data sequence of the same source) could only compress the image to 7, 710 bits.

In this paper, we propose EyeCerts, a biometric system for the identification of people which achieves offline verification of certified, cryptographically secure documents. An EyeCert is a printed document which certifies the association of content on the document with a biometric feature-a compressed version of a human iris in this work. The system is highly cost-effective since it does not require high complexity, hard-to-replicate printing technologies. Further, the device used to verify an EyeCert is inexpensive, estimated to have approximately the same cost as an off-the-shelf iris-scanning camera. As a central component of the EyeCert system, we present an iris analysis technique that aims to extract and compress the unique features of a given iris with a discrimination criterion using limited storage. The compressed features should be at maximal distance with respect to a reference iris image database. The iris analysis algorithm performs several steps in three main phases: 1) the algorithm detects the human iris by using a new model which is able to compensate for the noise introduced by the surrounding eyelashes and eyelids, 2) it converts the isolated iris using a modified Fourier-Mellin transform into a standard domain where the common radial patterns of the human iris are concisely represented, and 3) it optimally selects, aligns, and near-optimally compresses the most distinctive transform coefficients for each individual user. Using a low-quality imaging system (sub-U.S.$100), a /spl chi//sup 2/ error distribution model, and assuming a fixed false negatives rate of 5%, EyeCert caused false positives at rates better than 10/sup -5/ and as low as 10/sup -30/ for certain users.

This paper proposes EyeCerts, a biometric system for identification of people, which achieves off-line verification of certified, cryptographically secure documents. An EyeCert is a printed document, which certifies the association of a given text with a biometric feature-a compressed version of a human iris in this work. As a central component of the EyeCert system, an iris analysis technique that extracts and compresses the unique features of a given iris using limited storage is presented. The compressed features should be at maximal distance with respect to a reference iris image database. The iris analysis algorithm performs several steps in three main phases: (i) it detects the human iris, (ii) it converts the isolated iris using a modified Fourier-Mellin transform into a standard domain where the common radial patterns of the human iris are concisely represented, and (iii) it optimally selects, aligns, and near-optimally compresses the most distinctive transform coefficients for each individual user. Using a low quality imaging system (sub-US$100) and developed and readily available low complexity processing techniques, the overall system is shown to have probabilities of false negative and false positive on the order of 10/sup -5/.

One of the prime reasons movie and music studios have ignored the Internet for open-networked multimedia content delivery, has been the lack of a technology that can support a secure digital rights management (DRM) system on a general purpose computer. The difficulty of building an effective multimedia DRM stems from the fact that traditional cryptograic primitives such as encryption or scrambling do not protect audio or video signals once they are played in plain-text. This fact, commonly referred to as "the analog hole," has been responsible for the popularity of multimedia file sharing which cannot be controlled, at least technically, by content's copyright owners.