Anindya Sarkar

Efficient and Robust Detection of Duplicate Videos in a Large Database

We present an efficient and accurate method for duplicate video detection in a large database using video fingerprints. We have empirically chosen the color layout descriptor, a compact and robust frame-based descriptor, to create fingerprints which are further encoded by vector quantization (VQ). We propose a new nonmetric distance measure to find the similarity between the query and a database video fingerprint and experimentally show its superior performance over other distance measures for accurate duplicate detection. Efficient search cannot be performed for high-dimensional data using a nonmetric distance measure with existing indexing techniques. Therefore, we develop novel search algorithms based on precomputed distances and new dataset pruning techniques yielding practical retrieval times. We perform experiments with a database of 38 000 videos, worth 1600 h of content. For individual queries with an average duration of 60 s (about 50% of the average database video length), the duplicate video is retrieved in 0.032 s, on Intel Xeon with CPU 2.33 GHz, with a very high accuracy of 97.5%.

Matrix Embedding With Pseudorandom Coefficient Selection and Error Correction for Robust and Secure Steganography

In matrix embedding (ME)-based steganography, the host coefficients are minimally perturbed such that the transmitted bits fall in a coset of a linear code, with the syndrome conveying the hidden bits. The corresponding embedding distortion and vulnerability to steganalysis are significantly less than that of conventional quantization index modulation (QIM)-based hiding. However, ME is less robust to attacks, with a single host bit error leading to multiple decoding errors for the hidden bits. In this paper, we employ the ME-RA scheme, a combination of ME-based hiding with powerful repeat accumulate (RA) codes for error correction, to address this problem. A key contribution of this paper is to compute log likelihood ratios for RA decoding, taking into account the many-to-one mapping between the host coefficients and an encoded bit, for ME. To reduce detectability, we hide in randomized blocks, as in the recently proposed Yet Another Steganographic Scheme (YASS), replacing the QIM-based embedding in YASS by the proposed ME-RA scheme. We also show that the embedding performance can be improved by employing punctured RA codes. Through experiments based on a couple of thousand images, we show that for the same embedded data rate and a moderate attack level, the proposed ME-based method results in a lower detection rate than that obtained for QIM-based YASS.

Adaptive MPEG-2 video data hiding scheme

We have investigated adaptive mechanisms for high-volume transform-domain data hiding in MPEG-2 video which can be tuned to sustain varying levels of compression attacks. The data is hidden in the uncompressed domain by scalar quantization index modulation (QIM) on a selected set of low-frequency discrete cosine transform (DCT) coefficients. We propose an adaptive hiding scheme where the embedding rate is varied according to the type of frame and the reference quantization parameter (decided according to MPEG-2 rate control scheme) for that frame. For a 1.5 Mbps video and a frame-rate of 25 frames/sec, we are able to embed almost 7500 bits/sec. Also, the adaptive scheme hides 20% more data and incurs significantly less frame errors (frames for which the embedded data is not fully recovered) than the non-adaptive scheme. Our embedding scheme incurs insertions and deletions at the decoder which may cause de-synchronization and decoding failure. This problem is solved by the use of powerful turbo-like codes and erasures at the encoder. The channel capacity estimate gives an idea of the minimum code redundancy factor required for reliable decoding of hidden data transmitted through the channel. To that end, we have modeled the MPEG-2 video channel using the transition probability matrices given by the data hiding procedure, using which we compute the (hiding scheme dependent) channel capacity.

Feature fusion and redundancy pruning for rush video summarization

This paper presents a video summarization technique for rushes that employs high-level feature fusion to identify segments for inclusion. It aims to capture distinct video events using a variety of features: k-means based weighting, speech, camera motion, significant differences in HSV color space, and a dynamic time warping (DTW) based feature that suppresses repeated scenes. The feature functions are used to drive a weighted k-means based clustering to identify visually distinct, important segments that constitute the final summary. The optimal weights corresponding to the individual features are obtained using a gradient descent algorithm that maximizes the recall of ground truth events from representative training videos. Analysis reveals a lengthy computation time but high quality results (60% average recall over 42 test videos) as based on manually-judged inclusion ofdistinct shots. The summaries were judged relatively easy to view and had an average amount of redundancy.

Secure Steganography: Statistical Restoration of the Second Order Dependencies for Improved Security

We present practical approaches for steganography that can provide improved security by closely matching the second-order statistics of the host rather than just the marginal distribution. The methods are based on the framework of statistical restoration, wherein a fraction of the host symbols available for hiding is actually used to restore the statistics; thus reducing the rate, but providing security against steganalysis. We establish correspondence between steganography and the earth-movers distance (EMD), a popular distance metric used in computer vision applications. The EMD framework can be used to define the optimum flow (modifications) of the host symbols for compensation. This formulation is used for image steganography by restoring the second-order statistics of the blockwise discrete cosine transform (DCT) coefficients. Some practical limitations of this approach (such as computational complexity and difficulty in dealing with overlapping coefficient pairs) are noted, and a new method is proposed that alleviates these deficiencies by identifying the coefficients to modify based on a local compensation criterion. Experimental results on several thousand natural images demonstrate the utility of the presented methods.

Estimation of optimum coding redundancy and frequency domain analysis of attacks for YASS - a randomized block based hiding scheme

Our recently introduced JPEG steganographic method called yet another steganographic scheme (YASS) can resist blind steganalysis by embedding data in the discrete cosine transform (DCT) domain in randomly chosen image blocks. To maximize the embedding rate for a given image and a specified attack channel, the redundancy factor used by the repeat- accumulate (RA) code based error correction framework in YASS is optimally chosen by the encoder. An efficient method is suggested for the decoder to accurately compute this redundancy factor. We also show experimentally which DCT coefficients are better suited for hiding and detection under various attacks. The effectiveness of YASS for robust steganography is demonstrated for certain attacks.

Improving re-sampling detection by adding noise

Current image re-sampling detectors can reliably detect re-sampling in JPEG images only up to a Quality Factor (QF) of 95 or higher. At lower QFs, periodic JPEG blocking artifacts interfere with periodic patterns of re-sampling. We add a controlled amount of noise to the image before the re-sampling detection step. Adding noise suppresses the JPEG artifacts while the periodic patterns due to re-sampling are partially retained. JPEG images of QF range 75-90 are considered. Gaussian/Uniform noise in the range of 28-24 dB is added to the image and the images thus formed are passed to the re-sampling detector. The detector outputs are averaged to get a final output from which re-sampling can be detected even at lower QFs. We consider two re-sampling detectors - one proposed by Poposcu and Farid [1], which works well on uncompressed and mildly compressed JPEG images and the other by Gallagher [2], which is robust on JPEG images but can detect only scaled images. For multiple re-sampling operations (rotation, scaling, etc) we show that the order of re-sampling matters. If the final operation is up-scaling, it can still be detected even at very low QFs.

Double embedding in the quantization index modulation framework

Quantization index modulation (QIM) is a commonly used data hiding technique where a single bit is embedded per coefficient. Here, we propose the use of double embedding in the QIM framework where a single coefficient is modified twice, using two quantizers, to embed two bits. The motivation behind substituting single embedding with double embedding in the QIM framework for a certain steganographic scheme is to increase its hiding rate without significantly increasing the embedding distortion and the stego schemes detectability against steganalysis. We empirically determine the best way to couple the double embedding framework with a repeat accumulate code based error correction scheme. For moderate noise levels, the use of double embedding is seen to be significantly advantageous over single embedding.

Adding Gaussian noise to “denoise” JPEG for detecting image resizing

A common problem affecting most image resizing detection algorithms is that they are susceptible to JPEG compression. This is because JPEG introduces periodic artifacts, as it works on 8×8 blocks. We propose a novel yet counter intuitive technique to “denoise” JPEG images by adding Gaussian noise. We add a suitable amount of Gaussian noise to a resized and JPEG compressed image so that the periodicity due to JPEG compression is suppressed while that due to resizing is retained. The controlled Gaussian noise addition works better than median filtering and weighted averaging based filtering for suppressing the JPEG induced periodicity.

Obtaining higher rates for steganographic schemes while maintaining the same detectability

This paper focuses on modifying the decoder module for an active steganographic scheme to increase the effective data-rate without affecting the embedding module. Three techniques are suggested to improve the error correction framework of an active steganographic scheme. The first involves puncturing where the code-length is increased by adding a suitable number of additional erasures. The second technique involves channel modeling and soft-decision decoding which is adaptive to the embeddable image coefficient. The third method adjusts the erasure threshold depending on the design hiding quantizer so as to achieve a higher data-rate. Combining these techniques, the effective data-rate is increased by 10%-50% for Yet Another Steganographic Scheme (YASS), a popular active steganographic scheme.