Sabu Emmanuel

Visual Object Tracking Based on Backward Model Validation

Appearance model updating is a challenging task in visual object tracking with occlusion and appearance variation. To avoid error accumulation in model updating, validation of updating is generally performed in tracking algorithms. These algorithms use the existing appearance model to validate incoming data. However, the existing appearance model may not be able to distinguish the valid training data (resulting from large appearance variation) from the invalid ones (resulting from occlusion), since both appearance variation and occlusion would lead to a good deal of appearance change of the estimated tracking result. The root of the problem is: the existing (outdated) model with information from frame 1 to n-1 may not be able to predict large appearance variations in frame n and, as a result, the appearance variations may be excluded from model updating. This defeats the purpose of model updating, which is to include new changes in appearance variations to the model, because the existing methods do not have the provision to include such changes in model updating by validating changes with the outdated model. To address this problem, we propose a backward model validation-based visual tracking (BVT) algorithm, which performs model updating first in frame n and then uses the information from the incoming frame (frame n + 1) to backward-check whether the updating is valid (occurrence of appearance variation) or invalid (occurrence of occlusion). In this way, the uncertainty of validating unpredictable features with the existing appearance models can be avoided. Moreover, an adaptive feature fusion method is designed to properly integrate the color-based feature with texture-based feature. The proposed feature extraction method provides a robust representation of the target with both rotation and shape deformation. Experimental results demonstrate that the proposed BVT algorithm outperforms the relevant existing algorithms on both publicly available and proprietary databases.

Function Level Control Flow Obfuscation for Software Security

Software released to the user has the risk of reverse engineering attacks. Software control flow obfuscation is one of the techniques used to make the reverse engineering of software programs harder. Control flow obfuscation, obscures the control flow of the program so that it is hard for an analyzer to decode the logic of the program. In this paper, we propose an obfuscation algorithm which obscures the control flow across functions. In our method code fragments from each function is stripped from the original function and is stored in another function. Each function will be having code fragments from different functions, thereby creating a function level shuffled version of the original program. Control flow is obscured between and within the function by this method. Experimental results indicate that the algorithm performs well against automated attacks.

Sensor Pattern Noise Estimation Using Probabilistically Estimated RAW Values

Photo response nonuniformity (PRNU) is consider as reliable camera fingerprint for identifying source of a digital images. Digital cameras use various image processing operations to map linear color measurements (raw data) into nonlinear narrow gamut image. This nonlinear transformation affects estimation of PRNU. To undo the effect of nonlinear transformation, in this letter, we propose to estimate PRNU from probabilistically obtained raw values. Since not all cameras provide raw values as their output, we propose to compute estimate of raw values from the JPEG images using probabilistic color derendering procedure. The estimated raw values are modeled as a Poisson process and then maximum likelihood estimation (MLE) is used for PRNU estimation. The experimental results show that, the digital camera identification using our proposed PRNU estimate is better than using other popular PRNU estimate.

ACE–An Effective Anti-forensic Contrast Enhancement Technique

Detecting Contrast Enhancement (CE) in images and anti-forensic approaches against such detectors have gained much attention in multimedia forensics lately. Several contrast enhancement detectors analyze the first order statistics such as gray-level histogram of images to determine whether an image is CE or not. In order to counter these detectors various anti-forensic techniques have been proposed. This led to a technique that utilized second order statistics of images for CE detection. In this letter, we propose an effective anti-forensic approach that performs CE without significant distortion in both the first and second order statistics of the enhanced image. We formulate an optimization problem using a variant of the well known Total Variation (TV) norm image restoration formulation. Experiments show that the algorithm effectively overcomes the first and second order statistics based detectors without loss in quality of the enhanced image.

Spatial domain quantization noise based image filtering detection

Smart image editing and processing techniques make it easier to manipulate an image convincingly and also hide any artifacts of tampering. Common real world forgeries can be accompanied by enhancement operations like filtering, compression and/or format conversion to suppress forgery artifacts. Out of these enhancement operations, filtering is very common and has received a lot of attention in forensics research lately. However, different filtering operations and image formats are not investigated deeply and simultaneously. We propose an algorithm to detect if a given image has undergone filtering based enhancement irrespective of the format of image or the type of filter applied. In the proposed algorithm, we exploit the correlation of spatial domain quantization noise of an image by extracting transition probability features and classify the image as filtered or unfiltered. Experiments are performed to evaluate the robustness and compare the performance of the proposed technique with popular forensic filtering detection algorithms and is found to be superior in most of the cases.

Obfuscation by code fragmentation to evade reverse engineering

Software distributed can be reverse engineered using software analyzing tools, thereby letting an adversary understand the logic of the program and finding the vulnerabilities in the program. Control flow obfuscation is one of the techniques that can obscure the program so that the reverse engineering tools get an erroneous result while analyzing the program. In this paper, we propose a binary obfuscation algorithm that makes it difficult to reverse engineer the programs. The basic idea is to fragment the binary code by moving instruction sets from various parts of the program to a new code block. The control flow, to and from the new code block, is camouflaged using dynamic instruction modification during runtime. Experimental results indicate that the algorithm performs well against reverse engineering attacks by standard tools.

Forensic Analysis of Linear and Nonlinear Image Filtering Using Quantization Noise

The availability of intelligent image editing techniques and antiforensic algorithms, make it convenient to manipulate an image and to hide the artifacts that it might have produced in the process. Real world forgeries are generally followed by the application of enhancement techniques such as filtering and/or conversion of the image format to suppress the forgery artifacts. Though several techniques evolved in the direction of detecting some of these manipulations, additional operations like recompression, nonlinear filtering, and other antiforensic methods during forgery are not deeply investigated. Toward this, we propose a robust method to detect whether a given image has undergone filtering (linear or nonlinear) based enhancement, possibly followed by format conversion after forgery. In the proposed method, JPEG quantization noise is obtained using natural image prior and quantization noise models. Transition probability features extracted from the quantization noise are used for machine learning based detection and classification. We test the effectiveness of the algorithm in classifying the class of the filter applied and the efficacy in detecting filtering in low resolution images. Experiments are performed to compare the performance of the proposed technique with state-of-the-art forensic filtering detection algorithms. It is found that the proposed technique is superior in most of the cases. Also, experiments against popular antiforensic algorithms show the counter antiforensic robustness of the proposed technique.

Robust PRNU estimation from probabilistic raw measurements

Abstract Images captured by digital cameras undergo various in-camera processing such as JPEG compression, white balancing, power transforms and other operations to map raw data into nonlinear small gamut image. Due to nonlinear transformation, artifacts or signatures used for camera identification also undergo a significant change. Photo Response Non-Uniformity (PRNU), proved to be useful for uniquely identifying the camera, also undergoes same in-camera operations. Hence estimation of PRNU is affected which often leads to rise in false identification. In this work, we develop a novel algorithm for robust estimation of PRNU from probabilistically obtained raw data. Since not all cameras provide raw data as their output, we compute raw data from the JPEG output using probabilistic color de-rendering procedure. The estimated raw data is modeled as a Poisson process, and Maximum Likelihood Estimation (MLE) is used for PRNU estimation. We then use our estimate of PRNU for identifying the camera using images. We also compare the performance of our algorithm with other state-of-the-art algorithms. Additionally, we demonstrate the robustness of estimate obtained by localizing the forgery in images. The extensive experimental analysis is performed over thousands of patches from various cameras to illustrate the efficiency of proposed approach, which effectively overcomes the state-of-the-art.

Detection of Malware Applications in Android Smartphones

Nowadays, smartphones are available at cheaper rates and are widely used across the world. Smartphones are not only used for making phone calls and sending messages but also for storing personal data, Internet browsing, online banking, etc. Hence, smartphones have become a potential target for cyberattack. Malware attacks in the smartphones have been growing at an alarming rate and the cybercriminals are targeting smartphones to spread malware for stealing money and confidential data stored in the phones. Therefore, it is essential to ensure security in mobile platforms. In this chapter, we discuss the current malware detection mechanisms in smartphones and their drawbacks.

Anti-forensic technique for median filtering using L 1 -L 2 TV model

Median filtering is one of the most popular image anti-forensic technique. Due to its nonlinearity, median filtering effectively hides traces of different types of image manipulation and therefore is integrated into several Anti-Forensic algorithms. As a result, the detection of median filtering has become of great significance for forensic analysis. To this end, several median filtering detectors have been proposed. In order to counter this, various anti-forensic techniques have also been suggested. In this paper we propose an effective anti-forensic technique that performs the median filtering operation on the image while ensuring minimal change in its spatial characteristics. The anti-forensic attack is formulated as an optimization problem and is constrained by a difference of Anisotropic and Isotropic Total Variation regularization. We demonstrate the effectiveness of the proposed technique by performing various experiments on popular median filtering detectors and observing the degradation of their performance on the anti-forensically generated images.