Yao-Chung Lin

Image Authentication Based on Distributed Source Coding

Image authentication is important in content delivery via untrusted intermediaries, such as peer-to-peer (P2P) file sharing. Many differently encoded versions of the original image might exist. On the other hand, intermediaries might tamper with the contents. Distinguishing the legitimate diversity of encodings from malicious manipulation is the challenge addressed in this paper. We develop a novel approach based on distributed source coding for the problem of backward-compatible image authentication. The key idea is to provide a Slepian-Wolf encoded quantized image projection as authentication data. This version can be correctly decoded only with the help of an authentic image as side information. Distributed source coding provides the desired robustness against legitimate encoding variations, while detecting illegitimate modification. We demonstrate false acceptance rates close to zero for authentication data sizes that are only a few percent of the compressed image size.

Image Authentication and Tampering Localization using Distributed Source Coding

Media authentication is important in content delivery via untrusted intermediaries, such as peer-to-peer (P2P) file sharing. Many differently encoded versions of a media file might exist. Our previous work applied distributed source coding to distinguish the legitimate diversity of encoded images from tampering. An authentication decoder was supplied with a Slepian-Wolf encoded lossy version of the image as authentication data. Distributed source coding provided the desired robustness against legitimate encoding variations, while detecting illegitimate modification. We augment the decoder to localize tampering in an image already deemed to be unauthentic. The localization decoder requires only incremental localization data beyond the authentication data since we use rate-adaptive distributed source codes. Both decoders perform joint bitplane decoding, rather than conditional bitplane decoding. Our results demonstrate that tampered image blocks can be identified with high probability using authentication plus localization data of only a few hundred bytes for a 512times512 image.

Image Authentication Using Distributed Source Coding

We present a novel approach using distributed source coding for image authentication. The key idea is to provide a Slepian-Wolf encoded quantized image projection as authentication data. This version can be correctly decoded with the help of an authentic image as side information. Distributed source coding provides the desired robustness against legitimate variations while detecting illegitimate modification. The decoder incorporating expectation maximization algorithms can authenticate images which have undergone contrast, brightness, and affine warping adjustments. Our authentication system also offers tampering localization by using the sum-product algorithm.

Fast Mode Decision for H.264 Based on Rate-Distortion Cost Estimation

A fast mode decision algorithm for an H.264 encoder is proposed which reduces computation by using a statistical dependency of macroblock rate-distortion (RD) costs. The algorithm skips the motion estimation and/or intra prediction mode decision with an adaptive threshold. The simulation results show that the proposed algorithm achieves an almost 2X speedup with negligible degradation in coding efficiency.

Reduced-reference image quality assessment using distributed source coding

This paper presents a reduced-reference image quality assessment scheme using distributed source coding for remotely monitoring image quality. In our scheme, an image server extracts a feature vector from the original image and transmits its Slepian-Wolf syndrome using an LDPC encoder. With the rate of the Slepian-Wolf bitstream chosen according to a predetermined admissible image quality, the receiver can reconstruct the feature vector using its received image, as side information, as long as the quality is higher than the admissible quality. Thus the receiver can determine the received image quality using the reconstructed feature vector. Simulation results show that distributed source coding can reduce the bit-rate of the feature vector by 50% and achieve better compression performance than conventional source coding.

Adaptive Distributed Source Coding

We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding.

Video quality monitoring for mobile multicast peers using distributed source coding

We consider a peer-to-peer multicast video streaming system in which untrusted intermediaries transcode video streams for heterogeneous mobile peers. Many different legitimate versions of the video might exist. However, there is the risk that the untrusted intermediaries might tamper with the video content. Quality estimation and tampering detection are important in this scenario. We propose that each mobile peer sends a digest of its received video to a quality monitoring server which has access to the original video. The digest is a Slepian-Wolf coded projection of the received video. Distributed source coding provides rate-efficient encoding of the projection by exploiting the correlation between the projections of the original and received videos. Two different projections are designed for quality estimation and tampering detection, respectively. We show that the projections can be encoded at a low rate of just a few kilobits per second. Compared to the ITU-T J.240 Recommendation for remote PSNR monitoring, our scheme achieves a bit-rate which is lower by at least one order of magnitude.

Audio authentication based on distributed source coding

Audio authentication is important in content delivery via untrusted intermediaries, for example peer-to-peer (P2P) file sharing. Many differently encoded versions of the original audio might exist. Distinguishing the legitimate diversity of encodings from malicious tampering is the challenge addressed in this paper. We develop an approach based on distributed source coding for the problem of backward-compatible audio authentication. The key idea is to provide a Slepian-Wolf encoded quantized perceptually significant audio projection as authentication data. This version can be correctly decoded only with the help of authentic audio as side information. Distributed source coding provides the desired robustness against legitimate encoding variations, while detecting illegitimate modification. We demonstrate reliable authentication at a Slepian- Wolf bitrate of less than 100 bit/s.

Authenticating contrast and brightness adjusted images using distributed source coding and expectation maximization

Media authentication is important in content delivery via untrusted intermediaries, such as peer-to-peer (P2P) file sharing. Many differently encoded versions of a media file might exist. Our previous work applied distributed source coding not only to distinguish the legitimate diversity of encoded images from tampering but also localize the tampered regions in an image already deemed to be inauthentic. An authentication decoder was supplied with a Slepian-Wolf encoded image projection as authentication data. We extend our scheme to authenticate contrast and brightness adjusted images using an expectation maximization algorithm. Experimental results demonstrate that the proposed algorithm can distinguish legitimate encodings of authentic contrast and brightness adjusted images from illegitimately modified versions using authentication data of about 100 bytes.

Compression of VQM features for low bit-rate video quality monitoring

Reduced reference video quality assessment techniques provide a practical and convenient way of evaluating the quality of a processed video. In this paper, we propose a method to efficiently compress standardized VQM (Video Quality Model) [1] features to bit-rates that are small relative to the transmitted video. This is achieved through two stages of compression. In the first stage, we remove the redundancy in the features by only transmitting the necessary original video features at the lowest acceptable resolution for the calculation of the final VQM value. The second stage involves using the features of the processed video at the receiver as side-information for efficient entropy coding and reconstruction of the original video features. Experimental results demonstrate that our approach achieves high compression ratios of more than 30× with small error in the final VQM values.