The Experts below are selected from a list of 1296 Experts worldwide ranked by ideXlab platform

Ruchira Naskar - One of the best experts on this subject based on the ideXlab platform.

  • Detection and localization of inter-frame forgeries in videos based on macroblock variation and motion vector analysis
    Computers & Electrical Engineering, 2021
    Co-Authors: Jamimamul Bakas, Ruchira Naskar, Sambit Bakshi
    Abstract:

    Abstract Surveillance videos and footages are the primary sources of evidence for any event or crime in the court of law. However, with the rapid advent of low-cost, computationally cheap video manipulating software and tools, video manipulation has become a no-brainer task today. This introduces a major challenge in authenticating the sanctity/originality of videos before they can be produced in the court, or used in other sensitive application domains. In this paper, we propose a Digital Forensic Technique to detect inter-frame forgeries in surveillance videos. The proposed Technique utilizes compressed domain video footprints i.e, prediction footprint variation and variation of motion vectors in videos, for the purpose of video forgery detection and localization. Through this work, we identify the type of forgery that has taken place in a video. We have performed experiment over 43 authentic and 720 inter-frame forged videos. Our experimental results indicate that the proposed Technique performs consistently efficiently, irrespective of the group of pictures length and degree of compression in videos.

  • a Digital Forensic Technique for inter frame video forgery detection based on 3d cnn
    International Conference on Information Systems Security, 2018
    Co-Authors: Jamimamul Bakas, Ruchira Naskar
    Abstract:

    With the present-day rapid growth in use of low-cost yet efficient video manipulating software, it has become extremely crucial to authenticate and check the integrity of Digital videos, before they are used in sensitive contexts. For example, a CCTV footage acting as the primary source of evidence towards a crime scene. In this paper, we deal with a specific class of video forgery detection, viz., inter-frame forgery detection. We propose a deep learning based Digital Forensic Technique using 3D Convolutional Neural Network (3D-CNN) for detection of the above form of video forgery. In the proposed model, we introduce a difference layer in the CNN, which mainly targets to extract the temporal information from the videos. This in turn, helps in efficient inter-frame video forgery detection, given the fact that, temporal information constitute the most suitable form of features for inter-frame anomaly detection. Our experimental results prove that the performance efficiency of the proposed deep learning 3D CNN model is \(97\%\) on an average, and is applicable to a wide range of video quality.

  • ICISS - A Digital Forensic Technique for Inter–Frame Video Forgery Detection Based on 3D CNN
    Information Systems Security, 2018
    Co-Authors: Jamimamul Bakas, Ruchira Naskar
    Abstract:

    With the present-day rapid growth in use of low-cost yet efficient video manipulating software, it has become extremely crucial to authenticate and check the integrity of Digital videos, before they are used in sensitive contexts. For example, a CCTV footage acting as the primary source of evidence towards a crime scene. In this paper, we deal with a specific class of video forgery detection, viz., inter-frame forgery detection. We propose a deep learning based Digital Forensic Technique using 3D Convolutional Neural Network (3D-CNN) for detection of the above form of video forgery. In the proposed model, we introduce a difference layer in the CNN, which mainly targets to extract the temporal information from the videos. This in turn, helps in efficient inter-frame video forgery detection, given the fact that, temporal information constitute the most suitable form of features for inter-frame anomaly detection. Our experimental results prove that the performance efficiency of the proposed deep learning 3D CNN model is \(97\%\) on an average, and is applicable to a wide range of video quality.

  • ICISS - A Deep Learning Based Digital Forensic Solution to Blind Source Identification of Facebook Images
    Information Systems Security, 2018
    Co-Authors: Venkata Udaya Sameer, Ishaan Dali, Ruchira Naskar
    Abstract:

    Source Camera Identification is a Digital Forensic way of attributing a contentious image to its authentic source, especially used in legal application domains involving terrorism, child pornography etc. The state–of–the–art source camera identification Techniques, however, are not suitable to work with images downloaded from online social networks. This is because, online social networks impart specific image artefacts, due to proprietary image compression requirements for storage and transmission, which prevents accurate Forensic source investigations. Moreover, each social network has its own compression standards, which are never made public due to ethical issues. This makes source identification task even more difficult for Forensic analysts. In present day and age, where there is abundant use of social networks for image transmission, it is high time that source camera identification with images downloaded from social networks, be efficiently addressed. In this paper, we propose a deep learning based Digital Forensic Technique for source camera identification, on images downloaded from Facebook. The proposed deep learning Technique is adapted from the popular ResNet50 network, which majorly consists of convolutional layers and a few pooling layers. Our experimental results prove that the proposed Technique outperforms the traditional source camera identification methods.

  • A Digital Forensic Technique for Inter–Frame Video Forgery Detection Based on 3D CNN
    Information Systems Security, 2018
    Co-Authors: Jamimamul Bakas, Ruchira Naskar
    Abstract:

    With the present-day rapid growth in use of low-cost yet efficient video manipulating software, it has become extremely crucial to authenticate and check the integrity of Digital videos, before they are used in sensitive contexts. For example, a CCTV footage acting as the primary source of evidence towards a crime scene. In this paper, we deal with a specific class of video forgery detection, viz., inter-frame forgery detection. We propose a deep learning based Digital Forensic Technique using 3D Convolutional Neural Network (3D-CNN) for detection of the above form of video forgery. In the proposed model, we introduce a difference layer in the CNN, which mainly targets to extract the temporal information from the videos. This in turn, helps in efficient inter-frame video forgery detection, given the fact that, temporal information constitute the most suitable form of features for inter-frame anomaly detection. Our experimental results prove that the performance efficiency of the proposed deep learning 3D CNN model is $$97\%$$ on an average, and is applicable to a wide range of video quality.

Pooja Malviya - One of the best experts on this subject based on the ideXlab platform.

  • Digital Forensic Technique for Double Compression Based JPEG Image Forgery Detection
    Information Systems Security, 2014
    Co-Authors: Pooja Malviya, Ruchira Naskar
    Abstract:

    In today's cyber world images and videos are the major sources of information exchange. The authenticity of Digital images and videos is extremely crucial in the legal industry, media world and broadcast industry. However, with huge proliferation of low-cost, easy-to-use image manipulating software the fidelity of Digital images is at stake. In this paper we propose a Technique to detect Digital forgery in JPEG images, based on "double-compression". We deal with JPEG images because JPEG is the standard storage format used in almost all present day Digital cameras and other image acquisition devices. JPEG compresses an image to optimize the storage space requirement. When an attacker or criminal alters some part of a JPEG image by any image-editing tool and rewrites it to memory, the forged or modified part gets doubly-compressed. In this paper, we exploit this double-compression in JPEG images to identify Digital forgery.

Pankaj Malviya - One of the best experts on this subject based on the ideXlab platform.

  • Digital Forensic Technique for Multiple Compression based JPEG Forgery
    2015
    Co-Authors: Pankaj Malviya
    Abstract:

    In today's Digital world Digital multimedia like, images, voice-notes and videos etc., are the major source of information/data exchange. The authenticity of these multimedia is greatly vital in the legitimate business, media world and broadcast industry. However, with enormous multiplication of ease, simple-to-utilize data manipulation tools and softwares lead to the faithfulness of Digital images is in question. In our work, we propose a Technique to identify Digital forgery or tampering in JPEG (Joint Photographic Experts Group) images which are based on multiple compression factor. We are dealing with the JPEG images on the grounds because JPEG is the standard storage format used in almost all present day Digital devices like Digital camera, camcorder, mobile devices and other image acquisition devices. JPEG compresses a image to the best compression in-order to manage the storage requirement. JPEG is a lossy compression standard. At the point when an assailant or criminal modifies some region/part of a JPEG image by any image processing tools and save it, the modified region of the image is doubly-compressed. In our work, we exploit this multiple compression in JPEG images to distinguish Digital forgery or falsification.

  • ICISS - Digital Forensic Technique for Double Compression Based JPEG Image Forgery Detection
    Information Systems Security, 2014
    Co-Authors: Pankaj Malviya, Ruchira Naskar
    Abstract:

    In today’s cyber world images and videos are the major sources of information exchange. The authenticity of Digital images and videos is extremely crucial in the legal industry, media world and broadcast industry. However, with huge proliferation of low-cost, easy–to–use image manipulating software the fidelity of Digital images is at stake. In this paper we propose a Technique to detect Digital forgery in JPEG images, based on ”double–compression”. We deal with JPEG images because JPEG is the standard storage format used in almost all present day Digital cameras and other image acquisition devices. JPEG compresses an image to optimize the storage space requirement. When an attacker or criminal alters some part of a JPEG image by any image–editing tool and rewrites it to memory, the forged or modified part gets doubly–compressed. In this paper, we exploit this double–compression in JPEG images to identify Digital forgery.

Wenyong Zhao - One of the best experts on this subject based on the ideXlab platform.

  • CCCV (1) - An Image Forensic Technique Based on 2D Lighting Estimation Using Spherical Harmonic Frames
    Communications in Computer and Information Science, 2015
    Co-Authors: Wenyong Zhao, Hong Liu
    Abstract:

    In this paper, a novel approach for exposing Digital image tampering based on the theory of spherical harmonic frames is presented. We describe a robust Technique for exposing Digital forgeries that we utilize the information along a 2D occluding contour and estimate the lighting feature using spherical harmonic frames. Spherical harmonic frames are generated by the rotation along the symmetry axes of a symmetry group. The lighting-based Digital Forensic Technique using spherical harmonic frames inherits the robust property of frames and improve the statistical results compared with spherical harmonic bases. Experimental results performed using spherical harmonic frames prove the robust measurements and discriminability of the complex lighting environments from synthetic data and real data. The application of identifying the tampered images reveals the improvement of our method.

  • an image Forensic Technique based on 2d lighting estimation using spherical harmonic frames
    CCF Chinese Conference on Computer Vision, 2015
    Co-Authors: Wenyong Zhao
    Abstract:

    In this paper, a novel approach for exposing Digital image tampering based on the theory of spherical harmonic frames is presented. We describe a robust Technique for exposing Digital forgeries that we utilize the information along a 2D occluding contour and estimate the lighting feature using spherical harmonic frames. Spherical harmonic frames are generated by the rotation along the symmetry axes of a symmetry group. The lighting-based Digital Forensic Technique using spherical harmonic frames inherits the robust property of frames and improve the statistical results compared with spherical harmonic bases. Experimental results performed using spherical harmonic frames prove the robust measurements and discriminability of the complex lighting environments from synthetic data and real data. The application of identifying the tampered images reveals the improvement of our method.

Jamimamul Bakas - One of the best experts on this subject based on the ideXlab platform.

  • Detection and localization of inter-frame forgeries in videos based on macroblock variation and motion vector analysis
    Computers & Electrical Engineering, 2021
    Co-Authors: Jamimamul Bakas, Ruchira Naskar, Sambit Bakshi
    Abstract:

    Abstract Surveillance videos and footages are the primary sources of evidence for any event or crime in the court of law. However, with the rapid advent of low-cost, computationally cheap video manipulating software and tools, video manipulation has become a no-brainer task today. This introduces a major challenge in authenticating the sanctity/originality of videos before they can be produced in the court, or used in other sensitive application domains. In this paper, we propose a Digital Forensic Technique to detect inter-frame forgeries in surveillance videos. The proposed Technique utilizes compressed domain video footprints i.e, prediction footprint variation and variation of motion vectors in videos, for the purpose of video forgery detection and localization. Through this work, we identify the type of forgery that has taken place in a video. We have performed experiment over 43 authentic and 720 inter-frame forged videos. Our experimental results indicate that the proposed Technique performs consistently efficiently, irrespective of the group of pictures length and degree of compression in videos.

  • a Digital Forensic Technique for inter frame video forgery detection based on 3d cnn
    International Conference on Information Systems Security, 2018
    Co-Authors: Jamimamul Bakas, Ruchira Naskar
    Abstract:

    With the present-day rapid growth in use of low-cost yet efficient video manipulating software, it has become extremely crucial to authenticate and check the integrity of Digital videos, before they are used in sensitive contexts. For example, a CCTV footage acting as the primary source of evidence towards a crime scene. In this paper, we deal with a specific class of video forgery detection, viz., inter-frame forgery detection. We propose a deep learning based Digital Forensic Technique using 3D Convolutional Neural Network (3D-CNN) for detection of the above form of video forgery. In the proposed model, we introduce a difference layer in the CNN, which mainly targets to extract the temporal information from the videos. This in turn, helps in efficient inter-frame video forgery detection, given the fact that, temporal information constitute the most suitable form of features for inter-frame anomaly detection. Our experimental results prove that the performance efficiency of the proposed deep learning 3D CNN model is \(97\%\) on an average, and is applicable to a wide range of video quality.

  • ICISS - A Digital Forensic Technique for Inter–Frame Video Forgery Detection Based on 3D CNN
    Information Systems Security, 2018
    Co-Authors: Jamimamul Bakas, Ruchira Naskar
    Abstract:

    With the present-day rapid growth in use of low-cost yet efficient video manipulating software, it has become extremely crucial to authenticate and check the integrity of Digital videos, before they are used in sensitive contexts. For example, a CCTV footage acting as the primary source of evidence towards a crime scene. In this paper, we deal with a specific class of video forgery detection, viz., inter-frame forgery detection. We propose a deep learning based Digital Forensic Technique using 3D Convolutional Neural Network (3D-CNN) for detection of the above form of video forgery. In the proposed model, we introduce a difference layer in the CNN, which mainly targets to extract the temporal information from the videos. This in turn, helps in efficient inter-frame video forgery detection, given the fact that, temporal information constitute the most suitable form of features for inter-frame anomaly detection. Our experimental results prove that the performance efficiency of the proposed deep learning 3D CNN model is \(97\%\) on an average, and is applicable to a wide range of video quality.

  • A Digital Forensic Technique for Inter–Frame Video Forgery Detection Based on 3D CNN
    Information Systems Security, 2018
    Co-Authors: Jamimamul Bakas, Ruchira Naskar
    Abstract:

    With the present-day rapid growth in use of low-cost yet efficient video manipulating software, it has become extremely crucial to authenticate and check the integrity of Digital videos, before they are used in sensitive contexts. For example, a CCTV footage acting as the primary source of evidence towards a crime scene. In this paper, we deal with a specific class of video forgery detection, viz., inter-frame forgery detection. We propose a deep learning based Digital Forensic Technique using 3D Convolutional Neural Network (3D-CNN) for detection of the above form of video forgery. In the proposed model, we introduce a difference layer in the CNN, which mainly targets to extract the temporal information from the videos. This in turn, helps in efficient inter-frame video forgery detection, given the fact that, temporal information constitute the most suitable form of features for inter-frame anomaly detection. Our experimental results prove that the performance efficiency of the proposed deep learning 3D CNN model is $$97\%$$ on an average, and is applicable to a wide range of video quality.