Image Source Identification Using Convolutional Neural Networks in IoT Environment

Wang, Yan; Sun, Qindong; Rong, Dongzhu; Li, Shancang; Xu, Li Da

doi:10.1155/2021/5804665

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…This model involves an augmentation in layer count, culminating in a 26‐layer architecture that exhibits an approximate accuracy of 99%. Reference 26 delves into the identification of small‐size image source cameras by subjecting saturated images, smooth images, and other variations to scrutiny, leveraging the ResNet framework. To discern the source with enhanced precision, 27 adopts a dual neural network approach, calculating a rank of similarity index between an unknown‐source input image and employing this mechanism to improve accuracy 28 .…”

Section: Conventional Approach For Isimentioning

confidence: 99%

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

Singh,

Sehgal

2024

Computational Intelligence

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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 × 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 × 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.

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Section: Conventional Approach For Isimentioning

confidence: 99%

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

Singh,

Sehgal

2024

Computational Intelligence

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“…In the online pattern, the scheduling of the UAV is determined by itself, which means the UAV must own higher computing ability as well as battery capacity. Fortunately, advanced machine learning technology [21,22] has made it possible to deploy a tiny neural network in the on-board system. For example, Wang et al [23] proposed a deep learning-based real-time path scheduling algorithm for UAVs, considering the fairness of task allocation among UAVs.…”

Section: Uav-aided Online Task Offloadingmentioning

confidence: 99%

Benefit-oriented task offloading in UAV-aided mobile edge computing: An approximate solution

Gao

Tao

Wang

et al. 2023

Peer-to-Peer Netw. Appl.

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Recently, adopting UAVs equipped with the edge computing platform to provide computing service has been considered as a promising approach for resource-limited devices in mobile edge computing (MEC). Unfortunately, the limited resources (e.g., energy, computing and communication) of the UAV may significantly restrict its service capability, which means it has to selectively provide task offloading service to achieve the maximal benefit. In this article, aiming at optimizing the overall benefit of the UAV in a single dispatch, we propose an approximate Benefit Maximizing Task Offloading (BMTO) algorithm, which jointly considers the trajectory scheduling of the UAV and the offloading strategy of tasks. Specially, the flight path of the UAV is decomposed into several hover sites, which are selected by a benefit-cost approach. And the offloading sequence of tasks is arranged to maximize the benefit of the UAV through a surrogate function, which is proved to be a nonnegative monotone submodular function. Thus we transform the original problem into a submodular maximization problem and theoretically prove that BMTO owns an approximation ratio of 1 results show that our proposed algorithm outperforms the benchmark algorithms in terms of total benefit as well as energy efficiency ratio.

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“…Additionally, forensic video analysis has shown to be more challenging than image analysis in terms of what it takes to make sense of the video's data. This is due to the fact that videos have more tightly compressed formats compared to picture formats [34]. An image frame is a series of images that make up the video that changes throughout time and evoke movement and change throughout time.…”

Section: Analyzing the Source Of Digitally Identification Videosmentioning

confidence: 99%

Deep Learning-based Cnn Multi-modal Camera Model Identification for Video Source Identification

Singh,

Sehgal

2023

IJCAI

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Here is a high demand for multimedia forensics analysts to locate the original camera of photographs and videos that are being taken nowadays. There has been considerable progress in the technology of identifying the source of data, which has enabled conflict resolutions involving copyright infringements and identifying those responsible for serious offenses to be resolved. Video source identification is a challenging task nowadays due to easily available editing tools. This study focuses on the issue of identifying the camera model used to acquire video sequences used in this research that is, identifying the type of camera used to capture the video sequence under investigation. For this purpose, we created two distinct CNN-based camera model recognition techniques to be used in an innovative multi-modal setting. The proposed multi-modal methods combine audio and visual information in order to address the identification issue, which is superior to mono-modal methods which use only the visual or audio information from the investigated video to provide the identification information. According to legal standards of admissible evidence and criminal procedure, Forensic Science involves the application of science to the legal aspects of criminal and civil law, primarily during criminal investigations, in line with the standards of admissible evidence and criminal procedure in the law. It is responsible for collecting, preserving, and analyzing scientific evidence in the course of an investigation. It has become a critical part of criminology as a result of the rapid rise in crime rates over the last few decades. Our proposed methods were tested on a well-known dataset known as the Vision dataset, which contains about 2000 video sequences gathered from various devices of varying types. It is conducted experiments on social media platforms such as YouTube and WhatsApp as well as native videos directly obtained from their acquisition devices by the means of their acquisition devices. According to the results of the study, the multimodal approaches suggest that they greatly outperform their mono-modal equivalents in addressing the challenge at hand, constituting an effective approach to address the challenge and offering the possibility of even more difficult circumstances in the future.Povzetek: Razvita je metoda prepoznavanje izvornih kamer videoposnetkov s kombiniranjem zvočnih in vizualnih informacij z uporabo dveh DNN CNN tehnik.

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Image Source Identification Using Convolutional Neural Networks in IoT Environment

Cited by 7 publications

References 26 publications

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

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

Benefit-oriented task offloading in UAV-aided mobile edge computing: An approximate solution

Deep Learning-based Cnn Multi-modal Camera Model Identification for Video Source Identification

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