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.

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.

A rechargeable battery may be used to partially protect the privacy of information contained in a household's electrical load profile. We represent the system as a finite state model to make tractable the computation of the rate of information leakage. Specifically, we use a trellis algorithm to estimate the mutual information rate between the battery's input and output loads. We show that stochastic battery policies can leak 26% less information than a so-called best-effort algorithm (that holds the output load constant whenever possible). We finally describe the extension of the technique to more realistic models of the battery system.

Effective adaptive streaming systems need informative feedback that supports selection of appropriate actions. Packet level timing and reception statistics are already widely reported in feedback. In this paper, we introduce a method to produce low bit-rate visual quality feedback and evaluate its effectiveness in controlling errors in live video multicast. The visual quality feedback is a digest of picture content, and allows localized comparison in time and space on a continuous basis. This conveniently allows detection and localization of significant errors that may have originated from earlier irrecoverable losses, a task that is typically challenging with packet level feedback only. Our visual quality feedback has low bit overhead, at about 1% for high-definition video encoded at typical rates. For live video multicast with 10 clients, our experimental results show that the added ability to detect and correct large drift errors significantly reduces the resulting visual quality fluctuations.

Consider distributed source coding in which each block of the source at the encoder is associated with multiple candidates for side information at the decoder, just one of which is statistically dependent on the source block. Our encoder codes the source as syndrome bits and also sends a portion of it uncoded as doping bits. The decoder adaptively discovers the best side information candidates for each block of the source. The main contribution is a method based on density evolution to analyze and design the coding performance. Experimental results show that the density evolution technique is accurate in modeling the codec and optimizing its doping rate.

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 to localize tampered regions in an image already deemed to be in authentic. In both cases, authentication requires a Slepian-Wolf encoded image projection that is supplied to the decoder. We extend our scheme to authenticate images that have undergone affine warping. Our approach incorporates an expectation maximization algorithm into the Slepian-Wolf decoder. Experimental results demonstrate that the proposed algorithm can distinguish legitimate encodings of authentic images from illegitimately modified versions, despite an arbitrary affine warping, using authentication data of less than 250 bytes per image.

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 to localize tampered regions in an image already deemed to be inauthentic. In both cases, authentication requires a Slepian-Wolf encoded image projection that is supplied to the decoder. We extend our scheme to authenticate images that have undergone contrast, brightness, and affine warping adjustment. Our approach incorporates an expectation maximization algorithm into the Slepian-Wolf decoder. Experimental results demonstrate that the proposed algorithm can distinguish legitimate encodings of authentic images from illegitimately modified versions, despite arbitrary contrast, brightness, and affine warping adjustment, using authentication data of less than 250 bytes per image.

Distributed coding of correlated grayscale stereo images is effectively addressed by a recently proposed codec that learns block-wise disparity at the decoder. Based on the Slepian-Wolf theorem, one image can be transmitted at a rate approaching the conditional entropy if the other image is referenced as side information at the decoder. This paper improves the methods in the decoder design by refining disparity estimates to pixel resolution, generating more accurate initial disparity estimates, and modeling noise as a nonstationary random field. The new decoder enables up to an additional 9 percent bit rate savings for lossless coding. When the rate is insufficient for lossless reconstruction, the new decoder improves PSNR and significantly reduces visually unpleasant blocking artifacts.

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.

We pose the problem of tracing traitors, who have colluded to circumvent a multimedia fingerprinting system, as a sparse underdetermined linear problem. We propose a range of detection algorithms, based on sparse signal approximation, that span a tradeoff between performance and complexity. These algorithms are superior to conventional detection by correlation because they are collusion-aware. The simplest algorithm among them is more expensive than correlation by only a constant factor, and the second simplest one is more expensive by only a factor linear in the maximum number of traitors. We demonstrate that our proposed algorithms extend the robustness of already deployed fingerprinting schemes under both linear and nonlinear collusion attacks. For example, roughly twice as many traitors can be traced reliably than by using correlation, under mean or median collusion followed by compression.

Wyner-Ziv coding of multiview images avoids communications between source cameras. To achieve good compression performance, the decoder must relate the source and side information images. Since correlation between the two images is exploited at the bit level, it is desirable to map small Euclidean distances between coefficients into small Hamming distances between bitwise codewords. This important mapping property is not achieved with the binary code but can be achieved with the Gray code. Comparing the two mappings, it is observed that the Gray code offers a substantial benefit for unsupervised learning of unknown disparity but provides limited advantage if disparity is known. Experimental results with multiview images demonstrate the Gray code achieves PSNR gains of 2 dB over the binary code for unsupervised learning of disparity.

Media authentication and tampering localization are 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 authenticate the legitimate diversity of encoded images and also localize the tampered regions in an inauthentic image. The authentication/localization decoder was supplied with a Slepian-Wolf encoded image projection as authentication data. We extend our scheme to localize tampering in contrast and brightness adjusted images using an expectation maximization algorithm at the decoder. Experimental results demonstrate that tampered image blocks can be identified with high probability using data of only a few hundred bytes for a 512 times 512 image.

Wyner-Ziv coding of multiview images is an attractive solution because it avoids communications between individual cameras. To achieve good rate-distortion performance, the Wyner-Ziv decoder must reliably estimate the disparities between the multiview images. For the scenario where two reference images exist at the decoder, we propose a codec that effectively performs unsupervised learning of the two disparities between an image being Wyner-Ziv coded and the two reference images. The proposed two-disparity decoder disparity-compensates the two references images and generates side information more accurately than an existing one-disparity decoder. Experimental results with real multiview images demonstrate that the proposed codec achieves PSNR gains of 1-5 dB over the one-disparity codec.

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.

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.

We investigate retransmission-based robust video streaming over lossy packet networks in this paper. We propose to send a thumbnail video along with the video packets. The thumbnail video is Wyner-Ziv-coded to exploit its correlation with the primary video. The receiver decodes the primary video with the help of error concealment to mitigate packet losses. Upon receiving and successfully decoding the thumbnail, the receiver can estimate the local distortion due to packet losses and make intelligent decisions on which packets are needed for retransmission. Additional gain in video quality can be achieved by using the thumbnail to aid error concealment. Our experimental results demonstrate gains over previously proposed distortion-unaware heuristic methods.

We consider a new problem in network image coding for multicast. In a multihop mesh network, structured as a directed graph, all nodes decode and display reconstructions of the image (at possibly different qualities). Each node may also perform transcoding before transmitting data downstream in the network. The problem is the design of the coding and transcoding schemes to deliver the best image quality over the network. For a network with diamond topology, we show that multiple description coding combined with Wyner-Ziv transcoding is often superior to other methods. We argue further that the benefits are magnified for larger networks containing one or more diamond subnets. Our image coding experiments demonstrate that multiple description coding with Wyner-Ziv transcoding outperforms single description coding or multiple description coding with conventional transcoding, for both a diamond network and a two-hop mesh network with four branches.

Distributed compression is particularly attractive for stereo images since it avoids communication between cameras. Since compression performance depends on exploiting the redundancy between images, knowing the disparity is important at the decoder. Unfortunately, distributed encoders cannot calculate this disparity and communicate it. We consider the compression of grayscale stereo images, and develop an expectation maximization algorithm to perform unsupervised learning of disparity during the decoding procedure. Towards this, we devise a novel method for joint bitplane distributed source coding of grayscale images. Our experiments with both natural and synthetic 8-bit images show that the unsupervised disparity learning algorithm outperforms a system which does no disparity compensation by between 1 and more than 3 bits/pixel and performs nearly as well as a system which knows the disparity through an oracle

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.

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.

Distributed compression is particularly attractive for stereoscopic images since it avoids communication between cameras. Since compression performance depends on exploiting the redundancy between images, knowing the disparity is important at the decoder. Unfortunately, distributed encoders cannot calculate this disparity and communicate it. We consider a toy problem, the compression of random dot stereograms, and propose an expectation maximization algorithm to perform unsupervised learning of disparity during the decoding procedure. Our experiments show that this can achieve twice as efficient compression compared to a system with no disparity compensation and perform nearly as well as a system which knows the disparity through an oracle

This paper studies the combination of practical trellis and convolution codes with Tomlinson-Harashima precoding (THP) for the presubtraction of multiuser interference that is known at the transmitter but not known at the receiver. It is well known that a straightforward application of THP suffers power, modulo, and shaping losses. This paper proposes generalizations of THP that recover some of these losses. At a high signal-to-noise ratio (SNR), the precoding loss is dominated by the shaping loss, which is about 1.53 dB. To recover shaping loss, a trellis-shaping technique is developed that takes into account the knowledge of a noncausal interfering sequence, rather than just the instantaneous interference. At rates of 2 and 3 bits per transmission, trellis shaping is shown to be able to recover almost all of the 1.53-dB shaping loss. At a low SNR, the precoding loss is dominated by power and modulo losses, which can be as large as 3-4 dB. To recover these losses, a technique that incorporates partial interference presubtraction (PIP) within convolutional decoding is developed. At rates of 0.5 and 0.25 bits per transmission, PIP is able to recover 1-1.5 dB of the power loss. For intermediate SNR channels, a combination of the two schemes is shown to recover both power and shaping losses.