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Open Access
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Vishwajeet Mhetre, Sudershan Dolli, Sharvani Mahajan, Vyankatesh Kalme,
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- Student, Assistant Professor, Student, Assistant Professor Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune Maharashtra, Maharashtra, Maharashtra, Maharashtra India, India, India, India
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Abstract
nThe advent of digital media has necessitated advancements in forensic imaging, especially for the detection and verification of image authenticity. In this context, digital image forensics plays a critical role in identifying manipulated or counterfeit images. This paper presents a new method that uses deep learning techniques to enhance image forgery detection. The approach utilizes a convolutional neural network (CNN) to automatically learn and recognize the intricate features present in both authentic and altered images. The network is meticulously trained on a diverse dataset encompassing a wide array of real and fake images. These images undergo various manipulation techniques, including splicing, copy-move, and retouching, ensuring comprehensive exposure to potential forgeries. By analyzing subtle pixel-level discrepancies and spatial relationships, the CNN is equipped to discern authentic images from those that have been tampered with. Our approach utilizes the Busternet deep learning architecture, renowned for its efficacy in image analysis, to develop a sophisticated copy-move forgery detection system. This architecture excels in recognizing minute differences in image regions, enabling precise localization and verification of altered segments. The experimental results demonstrate the model’s superior performance in detecting forgeries, highlighting its potential as a powerful tool in digital forensic investigations. This work underscores the significant promise of deep learning applications in forensic imaging, paving the way for enhanced methods of image authenticity verification and contributing to the broader field of digital forensics.
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Keywords: Busternet, Convolution Neural Networks (CNN), Deep learning, Image forgery detection (IFD)
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Advances in Shell Programming(joasp)]
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References
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[6] Abidin AB, Majid HB, Samah AB, Hashim HB. Copy-move image forgery detection using deep learning methods: a review. In2019 6th international conference on research and innovation in information systems (ICRIIS) 2019 Dec 2 (pp. 1-6). IEEE.
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | July 3, 2024 | |
| Accepted | July 25, 2024 | |
| Published | August 1, 2024 |
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