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Innovative Deep Learning Solutions for Image Forgery Detection
编号：138 访问权限：仅限参会人 更新：2024-09-27 09:24:12 浏览：339次 拓展类型1

报告开始：暂无开始时间（Asia/Bangkok）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

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摘要
Digital image forgery detection is crucial in addressing the rapid spread of fake information through manipulated images, especially on social media platforms. Traditional techniques often focus on specific types of forgery, limiting their effectiveness in real-world scenarios. Traditional methods heavily depend on manual feature engineering, which often results in overlooked manipulations, decreased accuracy, adaptability, and scalability issues when handling large datasets or high-resolution images. Deep learning has emerged as a powerful tool for addressing the challenges associated with image forgery detection. The proposed work introduces an innovative method for detecting image forgeries using deep learning techniques, employing convolutional neural networks (CNNs) and specifically evaluating the performance of the EfficientNetb7 model. This method leverages transfer learning to detect copy-move image forgery. It involves generating featured images by calculating the difference between the input image and compressed versions, which are then fed into pre-trained CNN model. The model undergo fine-tuning to adapt to forgery detection. Additionally, the output of the forgery detection process includes both text and audio. This combination enhances the accessibility and interpretability of the detection results, making them more understandable for users with different sensory preferences or impairments. This added feature ensures that the detection outcomes are easily comprehensible and usable across a broader range of users and applications.  
关键词
Rapid Spread; Forged Detection; EfficientNetb7; CNN; Deep Learning; Digital Image.
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稿件作者
Fatima Hashim Abbas Al-Mustaqbal University
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