Department of Computer Science & Engineering

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IIT Jodhpur

Recently, at FSE '08, Nikolić and Biryukov introduced a new technique for analyzing SHA-2 round function. Building on their work, but using other differential paths, we construct two different deterministic attacks against 21-step SHA-2 hash family. Since the attacks are deterministic, they are actually combinatorial constructions of collisions. There are six free words in our first construction. This gives exactly 2192 different collisions for 21-step SHA-256 and exactly 2384 different collisions for 21-step SHA-512. The second construction has five free words. The best previous result, due to Nikolić and Biryukov, for finding collisions for 21-step SHA-256 holds with probability 2-∈19. No results on 21-step SHA-512 are previously known. Further, we provide evidence that the Nikolić-Biryukov differential path is unlikely to yield 21-step collisions for SHA-512. © 2008 Springer-Verlag Berlin Heidelberg.

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Deterministic constructions of 21-step collisions for the SHA-2 hash family

This article proposes a handcrafted fuzzy rule-based system for segmentation and identification of different tissue types in magnetic resonance (MR) brain images. The proposed fuzzy system uses a combination of histogram and spatial neighborhood-based features. The intensity variation from one type of tissue to another is gradual at the boundaries due to the inherent nature of the MR signal (MR physics). A fuzzy rule-based approach is expected to better handle these variations and variability in features corresponding to different types of tissues. The proposed segmentation is tested to classify the pixels of the T2-weighted axial MR images of the brain into three primary tissue types: white matter, gray matter and cerebral-spinal fluid. The results are compared with those from manual segmentation by an expert, demonstrating good agreement between them. © 2008 Elsevier Inc. All rights reserved.

Magnetic Resonance Imaging

Handcrafted fuzzy rules for tissue classification

In this paper, we address the problem of unsupervised learning of usual patterns of activities in an area under surveillance and detecting deviant patterns. We use video epitomes for segmenting foreground objects from background and obtain an approximate shape, trajectory and temporal information in the form of space-time patches. We apply pLSA for finding correlations among these patches to learn usual activities in the scene. We also extend pLSA to classify a novel video as usual or unusual. © 2008 IEEE.

Proceedings - 6th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2008

Unusual activity analysis using video epitomes and pLSA

We present a classifier unifying local features based representation and subspace based learning. We also propose a novel method to merge kernel eigen spaces (KES) in feature space. Subspace methods have traditionally been used with the full appearance of the image. Recently local features based bag-of-features (BoF) representation has performed impressively on classification tasks. We use KES with BoF vectors to construct class specific subspaces and use the distance of a query vector from the database KESs as the classification criteria. The use of local features makes our approach invariant to illumination, rotation, scale, small affine transformation and partial occlusions. The system allows hierarchy by merging the KES in the feature space. The classifier performs competitively on the challenging Caltech-101 dataset under normal and simulated occlusion conditions. We show hierarchy on a dataset of videos collected over the internet. © 2008 IEEE.

Proceedings - International Conference on Pattern Recognition

Bag-of-features kernel eigen spaces for classification

Smart cameras are expected to be important components for creating ubiquitous multimedia environments. In this paper, we propose a scheme for on-line semantic transcoding of the video captured by the smart camera. The transcoding process selects frames of importance and regions of interest for use by other processing elements in a ubiquitous computing environment. We have proposed a local associative computation based change detection scheme for identifying frames of interest. The algorithm also segments out the region of change. The computation is structured for easy implementation in DSP based embedded environment. The transcoding scheme enables the camera to communicate only regions of change in frames of interest to a server or a peer. Consequently communication and processing overhead reduces in a networked application environment. Experimental results have established effectiveness of the transcoding scheme. © 2008 IEEE.

Proceedings - 2008 International Symposium on Ubiquitous Multimedia Computing, UMC 2008

Video trans-coding in smart camera for ubiquitous multimedia environment

The novelty of the approach presented in this paper is the unique object-based video coding framework for videos obtained from a static camera. As opposed to most existing methods, the proposed method does not require explicit 2D or 3D models of objects and hence is general enough to satisfy the need for varying types of objects in the scene. The proposed system detects and tracks an object in the scene by learning the appearance model of each object online using nontraditional uniform norm based subspace. At the same time the object is coded using the projection coefficients to the orthonormal basis of the subspace learnt. The tracker incorporates a predictive framework based upon Kalman filter for predicting the five motion parameters. The proposed method shows substantially better compression than MPEG2 based coding with almost no additional complexity. © 2008 IEEE.

Parametric video compression using appearance space

The SHA-256 hash function has started getting attention recently by the cryptanalysis community due to the various weaknesses found in its predecessors such as MD4, MD5, SHA-0 and SHA-1. We make two contributions in this work. First we describe message modification techniques and use them to obtain an algorithm to generate message pairs which collide for the actual SHA-256 reduced to 18 steps. Our second contribution is to present differential paths for 19, 20, 21, 22 and 23 steps of SHA-256. We construct parity check equations in a novel way to find these characteristics. Further, the 19-step differential path presented here is constructed by using only 15 local collisions, as against the previously known 19-step near collision differential path which consists of interleaving of 23 local collisions. Our 19-step differential path can also be seen as a single local collision at the message word level. We use a linearized local collision in this work. These results do not cause any threat to the security of the SHA-256 hash function. © 2008 Springer-Verlag Berlin Heidelberg.

Attacking reduced round SHA-256

Fourier domain methods have had a long association with geometric vision. In this paper, we introduce Fourier domain methods into the field of visual servoing for the first time. We show how different properties of Fourier transforms may be used to address specific issues in traditional visual servoing methods, giving rise to algorithms that are more flexible. Specifically, we demonstrate how Fourier analysis may be used to obtain straight camera paths in the Cartesian space, do path following and correspondenceless visual servoing. Most importantly, by introducing Fourier techniques, we set a framework into which robust Fourier based geometry processing algorithms may be incorporated to address the various issues in servoing.© 2008 IEEE.

Frequency domain visual servoing using planar contours

In this work, we offer an approach to combine standard multimedia analysis techniques with knowledge drawn from conceptual metadata provided by domain experts of a specialized scholarly domain, to learn a domain-specific multimedia ontology from a set of annotated examples. A standard Bayesian network learning algorithm that learns structure and parameters of a Bayesian network is extended to include media observables in the learning. An expert group provides domain knowledge to construct a basic ontology of the domain as well as to annotate a set of training videos. These annotations help derive the associations between highlevel semantic concepts of the domain and low-level MPEG- 7 based features representing audio- visual content of the videos. We construct a more robust and refined version of this ontology by learning from this set of conceptually annotated videos. To encode this knowledge, we use MOWL, a multimedia extension of Web Ontology Language (OWL) which is capable of describing domain concepts in terms of their media properties and of capturing the inherent uncertainties involved. We use the ontology specified knowledge for recognizing concepts relevant to a video to annotate fresh addition to the video database with relevant concepts in the ontology. These conceptual annotations are used to create hyperlinks in the video collection, to provide an effective video browsing interface to the user. Copyright 2008 ACM.

Proceedings of the 1st International ACM Conference on Multimedia Information Retrieval, MIR2008, Co-located with the 2008 ACM International Conference on Multimedia, MM'08

Multimedia ontology learning for automatic annotation and video browsing

We present a novel learning-based framework for detecting interesting events in soccer videos. The input to the system is a raw soccer video. We have learning at three levels - learning to detect interesting low-level features from image and video data using Support Vector Machines (hereafter, SVMs), and a hierarchical Conditional Random Field-(hereafter, CRF-) based methodology to learn the dependencies of mid-level features and their relation with the lowlevel features, and high level decisions ('interesting events') and their relation with the mid-level features: all on the basis of training video data. Descriptors are spatio-temporal in nature - they can be associated with a region in an image or a set of frames. Temporal patterns of descriptors characterise an event. We apply this framework to parse soccer videos into Interesting (a goal or a goal miss) and Non-Interesting videos. We present results of numerous experiments in support of the proposed strategy. © 2008 IEEE.