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A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

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COMPUTATIONAL INTELLIGENCE 2024年第2期40卷 e12631-e12631页

作者： Singh, Surjeet Sehgal, Vivek Kumar Jaypee Univ Informat Technol Dept Comp Sci & Engn & Informat Technol Solan Himachal Prades India

With the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of image source identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine-learning methods to classify image sources. In the current landscape, data-driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT-generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 x 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 x 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.

关键词： classification image footprints analysis image source identification information retrieval internet of things introduction machine learning

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Virtual Sample Generation and Ensemble Learning Based image source identification With Small Training Samples

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INTERNATIONAL JOURNAL OF DIGITAL CRIME AND FORENSICS 2021年第3期13卷 34-46页

作者： Wu, Shiqi Wang, Bo Zhao, Jianxiang Zhao, Mengnan Zhong, Kun Guo, Yanqing Dalian Univ Technol Sch Informat & Commun Engn Dalian Liaoning Peoples R China Dalian Univ Technol Fac Management & Econ Dalian Liaoning Peoples R China Dalian Univ Technol Elect Informat Engn Dalian Liaoning Peoples R China Dalian Univ Technol Dalian Liaoning Peoples R China Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian Liaoning Peoples R China

Nowadays, source camera identification, which aims to identify the source camera of images, is quite important in the field of forensics. There is a problem that cannot be ignored that the existing methods are unreliable and even out of work in the case of the small training sample. To solve this problem, a virtual sample generation-based method is proposed in this paper, combined with the ensemble learning. In this paper, after constructing sub-sets of LBP features, the authors generate a virtual sample-based on the mega-trend-diffusion (MTD) method, which calculates the diffusion range of samples according to the trend diffusion theory, and then randomly generates virtual sample according to uniform distribution within this range. In the aspect of the classifier, an ensemble learning scheme is proposed to train multiple SVM-based classifiers to improve the accuracy of image source identification. The experimental results demonstrate that the proposed method achieves higher average accuracy than the state-of-the-art, which uses a small number of samples as the training sample set.

关键词： Ensemble Learning image source identification Mega-Trend-Diffusion Virtual Sample Generation

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image source identification with known post-processed based on convolutional neural network

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SIGNAL PROCESSING-image COMMUNICATION 2021年 99卷 116438-116438页

作者： Liao, Xin Chen, Jing Chen, Jiaxin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China

image source identification is important to verify the origin and authenticity of digital images. However, when images are altered by some post-processing, the performance of the existing source verification methods may degrade. In this paper, we propose a convolutional neural network (CNN) to solve the above problem. Specifically, we present a theoretical framework for different tampering operations, to confirm whether a single operation has affected photo response non-uniformity (PRNU) contained in images. Then, we divide these operations into two categories: non-influential operation and influential operation. Besides, the images altered by the combination of non-influential and influential operations are equal to images that have only undergone a single influential operation. To make our introduced CNN robust to both non-influential operation and influential operation, we define a multi-kernel noise extractor that consists of a high-pass filter and three parallel convolution filters of different sizes. The features generated by the parallel convolution layers are then fed to subsequent convolutional layers for further feature extraction. The experimental results provide the effectiveness of our method.

关键词： image source identification PRNU Tampered image Operation chain Convolutional neural network

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Exploring Feature Coupling and Model Coupling for image source identification

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2018年第12期13卷 3108-3121页

作者： Huang, Yonggang Cao, Longbing Zhang, Jun Pan, Lei Liu, Yuying Beijing Inst Technol Sch Comp Sci & Technol Beijing Engn Res Ctr High Volume Language Informa Beijing 100081 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Deakin Univ Sch Informat Technol Geelong Vic 3217 Australia

Recently, there has been great interest in feature-based image source identification. Previous statistical learning-based methods usually regarded the identification process as a classification problem. They assumed the dependence of features and the dependence of models. However, the two assumptions are usually problematic because of the genuine coupling of features and models. To address the issues, in this paper, we propose a novel image source identification scheme. For the feature coupling, a coupled feature representation is adopted to analyze the coupled interaction among features. The coupling relations among features and their powers are measured with Pearson's correlations and integrated in a Taylor-like expansion manner. Regarding model coupling, a new coupled probability representation is developed. The model coupling relationships are characterized with conditional probabilities induced by the confusion matrix and then combined with the law of total probability. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed scheme. Via mining the feature coupling and model coupling, the identification accuracy can be significantly improved.

关键词： image source identification feature coupling model coupling machine learning digital forensics

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Identifying natural images and computer generated graphics based on binary similarity measures of PRNU

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第1期78卷 489-506页

作者： Long, Min Peng, Fei Zhu, Yin Changsha Univ Sci & Technol Coll Comp & Commun Engn Changsha 410014 Hunan Peoples R China Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha 410114 Hunan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

Aiming at the identification of natural images and computer generated graphics, an image source pipeline forensics method based on binary similarity measures of PRNU (photo response non-uniformity) is proposed. As PRNU is a unique attribute of natural images, binary similarity measures of PRNU are used to represent the differences between natural images and computer generated graphics. Binary Kullback-Leibler distance, binary minimum histogram distance, binary absolute histogram distance and binary mutual entropy are calculated from PRNU in RGB three channels. With a total of 36 dimensions of features, LIBSVM is used for classification. Experimental results and analysis indicate that it can achieve an average identification accuracy of 99.83%, and the capability of identifying natural images and computer generated graphics is balanced. Meanwhile, it is robust against JPEG compression, rotation and additive noise.

关键词： image source identification Binary similarity measures Photo response non-uniformity noise (PRNU)

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Camera source identification of Digital images Based on Sample Selection

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第7期12卷 3268-3283页

作者： Wang, Zhihui Wang, Hong Li, Haojie Dalian Univ Technol Econ & Technol Dev Area DUT RU Int Sch Informat & Software Engn Dalian Peoples R China

With the advent of the Information Age, the source identification of digital images, as a part of digital image forensics, has attracted increasing attention. Therefore, an effective technique to identify the source of digital images is urgently needed at this stage. In this paper, first, we study and implement some previous work on image source identification based on sensor pattern noise, such as the Lukas method, principal component analysis method and the random subspace method. Second, to extract a purer sensor pattern noise, we propose a sample selection method to improve the random subspace method. By analyzing the image texture feature, we select a patch with less complexity to extract more reliable sensor pattern noise, which improves the accuracy of identification. Finally, experiment results reveal that the proposed sample selection method can extract a purer sensor pattern noise, which further improves the accuracy of image source identification. At the same time, this approach is less complicated than the deep learning models and is close to the most advanced performance.

关键词： image source identification sensor pattern noise random subspace method sample selection

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Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 71卷 72-81页

作者： Peng, Fei Zhou, Die-lan Long, Min Sun, Xing-ming Hunan Univ Sch Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Hunan Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

The aim of the work presented in this paper is to discriminate natural images (NI) and computer generated graphics (CG). The texture differences are analyzed to the residual images of NI and CG. The residual images are first extracted by using multiple linear regressions, and then the fitting degree of the regression model is investigated. Through the analysis of the difference of their residual images, 9 dimensions,of histogram features and 9 dimensions of multi-fractal spectrum features are extracted to represent their texture differences. Combined with 6 dimensions of regression model fitness features, natural images and computer generated graphics are discriminated by using a support vector machine (SVM) classifier. Experimental results and analysis show that it can achieve an average identification accuracy of 98.69%, and it is robust against JPEG compression, rotation, additive noise and image resizing. Compared with some existed methods, the selection of features is effective and fewer features are required for representing the differences between NI and CG. Meanwhile, the classification time is significantly reduced and the robustness is maintained. It has great potential to be used in image source pipeline identification. (C) 2016 Elsevier GmbH. All. rights reserved.

关键词： Digital image forensics image source identification Multifractal Regression analysis Natural images Computer generated graphics

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IDENTIFYING PHOTOREALISTIC COMPUTER GRAPHICS USING CONVOLUTIONAL NEURAL NETWORKS 24

IDENTIFYING PHOTOREALISTIC COMPUTER GRAPHICS USING CONVOLUTI...

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24th IEEE International Conference on image Processing (ICIP)

作者： Yu, In-Jae Kim, Do-Guk Park, Jin-Seok Hou, Jong-Uk Choi, Sunghee Lee, Heung-Kyu Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9781509021758

As computer graphics technology advances, it is becoming increasingly difficult to determine whether a given picture was taken by camera or via computer graphics. In this work, we propose a method to using simple CNN structures to identify photorealistic computer graphics (PRCG) using convolutional neural networks (CNN). This network trained to identify the source of image patches. We showed the network without pooling layer showed 98.2% accuracy, which is 2.1% higher than the result of using conventional object-recognition network. Testing random patches from image, the accuracy of identifying image reached 98.5%. Furthermore, it is possible to detect the photograph-PRCG synthesized regions from the image.

关键词： Digital Forensics image source identification Convolutional Neural Networks Photo-Realistic Computer Graphics

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PIZZARO: Forensic analysis and restoration of image and video data

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FORENSIC SCIENCE INTERNATIONAL 2016年 264卷 153-166页

作者： Kamenicky, Jan Bartos, Michal Flusser, Jan Mahdian, Babak Kotera, Jan Novozamsky, Adam Saic, Stanislav Sroubek, Filip Sorel, Michal Zita, Ales Zitova, Barbara Sima, Zdenek Svarc, Petr Horinek, Jan Acad Sci Czech Republ Inst Informat Theory & Automat Vodarenskou Vezi 4 CR-18208 Prague Czech Republic Inst Criminalist Audio Video Dept Prague Czech Republic Natl Drug Headquarters Criminal Police & Invest S Dept Informat Prague Czech Republic

This paper introduces a set of methods for image and video forensic analysis. They were designed to help to assess image and video credibility and origin and to restore and increase image quality by diminishing unwanted blur, noise, and other possible artifacts. The motivation came from the best practices used in the criminal investigation utilizing images and/or videos. The determination of the image source, the verification of the image content, and image restoration were identified as the most important issues of which automation can facilitate criminalists work. Novel theoretical results complemented with existing approaches (LCD re-capture detection and denoising) were implemented in the PIZZARO software tool, which consists of the image processing functionality as well as of reporting and archiving functions to ensure the repeatability of image analysis procedures and thus fulfills formal aspects of the image/video analysis work. Comparison of new proposed methods with the state of the art approaches is shown. Real use cases are presented, which illustrate the functionality of the developed methods and demonstrate their applicability in different situations. The use cases as well as the method design were solved in tight cooperation of scientists from the Institute of Criminalistics, National Drug Headquarters of the Criminal Police and Investigation Service of the Police of the Czech Republic, and image processing experts from the Czech Academy of Sciences. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

关键词： image forensic analysis image restoration image tampering detection image source identification

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identification of Natural images and Computer-Generated Graphics Based on Statistical and Textural Features

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JOURNAL OF FORENSIC SCIENCES 2015年第2期60卷 435-443页

作者： Peng, Fei Li, Jiao-ting Long, Min Hunan Univ Sch Comp Sci & Engn Changsha 410082 Hunan Peoples R China ChangSha Univ Sci & Technol Coll Comp & Commun Engn Changsha 410114 Hunan Peoples R China

To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics.

关键词： forensic science image source identification natural images computer-generated graphics multifractal dimension lacunarity analysis

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