Block and Fuzzy Techniques Based Forensic Tool for Detection and Classification of Image Forgery

Hashmi, Mohammad Farukh,Keskar, Avinash G. The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.4

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

Block and Fuzzy Techniques Based Forensic Tool for Detection and Classification of Image Forgery

Mohammad Farukh Hashmi,Avinash G. Keskar 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.4

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

Heterogeneous Sensor Data Analysis Using Efficient Adaptive Artificial Neural Network on FPGA Based Edge Gateway

( Nikhil B. Gaikwad ),( Varun Tiwari ),( Avinash Keskar ),( Nc Shivaprakash ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.10

We propose a FPGA based design that performs real-time power-efficient analysis of heterogeneous sensor data using adaptive ANN on edge gateway of smart military wearables. In this work, four independent ANN classifiers are developed with optimum topologies. Out of which human activity, BP and toxic gas classifier are multiclass and ECG classifier is binary. These classifiers are later integrated into a single adaptive ANN hardware with a select line(s) that switches the hardware architecture as per the sensor type. Five versions of adaptive ANN with different precisions have been synthesized into IP cores. These IP cores are implemented and tested on Xilinx Artix-7 FPGA using Microblaze test system and LabVIEW based sensor simulators. The hardware analysis shows that the adaptive ANN even with 8-bit precision is the most efficient IP core in terms of hardware resource utilization and power consumption without compromising much on classification accuracy. This IP core requires only 31 microseconds for classification by consuming only 12 milliwatts of power. The proposed adaptive ANN design saves 61% to 97% of different FPGA resources and 44% of power as compared with the independent implementations. In addition, 96.87% to 98.75% of data throughput reduction is achieved by this edge gateway.

Ghost Vehicle and Shadow Removal Approach for Traffic Surveillance and Monitoring at Various Intersections Using Computer Vision

Mohammad Farukh Hashmi,Avinash G. Keskar,Ravula Sai Kiran Reddy,Ambati Uday Kaushik 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.3

As traffic surveillance technology continues to grow worldwide vehicle detection, counting, tracking and classification are gaining importance. This paper proposes computer vision based real time vehicle detection, tracking and classification at urban intersections. Firstly, foreground extraction using double subtraction method is proposed, which increases the accuracy of blob detection. Classification based on the geometrical attributes of the vehicle and also quadrant division of the junction is put forward. Setting up dynamic ROIs is discussed, which increased the scope of traffic surveillance for different types of junctions. The proposed system is implemented using Intel Open CV library for image processing and video processing applications. The Practical implementation of the algorithm is made with C++ and computer vision. Several junction surveillance videos are used to evaluate the performance of the traffic surveillance system. In this paper, detection, tracking and classification of objects in with removal of shadow and ghost vehicles at different junction in video surveillance .Proposed work elaborated computer vision approach for Traffic monitoring in traffic surveillance application. Test results in the performance of the proposed algorithm in detection, classification and counting and proved the effectiveness of the traffic surveillance system. Obtained results showed a better performance in terms of accuracy.

Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier

Mohammad Farukh Hashmi,Avinash G. Keskar 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.4

Image forgery is a major issue in today's digital publishing and printing. Now a day’s system can be used for forensic purpose to validate the authenticity of an image. In this paper we present an approach for image forgery authentication. We observe that a non morphed and non forged image shows homogeneity in non spectral domain. This homogeneity is lost when any forgery or morphing is applied on the images. We therefore apply a set of transform over the images. We combine DCT statistics, LBP features with curvelet statistics and Gabor transform of the images to represent an image in the transformed domain. CASIA image dataset with seven thousand authentic and same numbers of tempered images is used to verify the technique. We divide the dataset into equal halves to perform training and testing. Transformed images are used to train Hidden Markov model as HMM can extract probabilistic state information from a large statistical model. A test images is tested in transformed domain by HMM with log likelihood estimator. In case HMM returns an indeterminist result or multiple subset of result, the transformed test image is tested with two class SVM classifier with RBF kernel. Results show that the accuracy of the system is over 89% for 500 test instances.