An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection

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Yogita Shelar
Prashant Sharma
Chandan Singh. D. Rawat


As the field of image processing and computer vision continues to develop, we are able to create edited images that seem more natural than ever before. Identifying real photos from fakes has become a formidable obstacle. Image forgery has become more common as the multimedia capabilities of personal computers have developed over the previous several years. This is due to the fact that it is simpler to produce fake images. Since image object fabrication might obscure critical evidence, techniques for detecting it have been intensively investigated for quite some time. The publicly available datasets are insufficient to deal with these problems adequately. Our work recommends using a deep learning based image inpainting technique to create a model to detect fabricated images. To further detect copy-move forgeries in images, we use an CNN-LSTM and Improved VGG adaptation network. Our approach could be useful in cases when classifying the data is impossible. In contrast, researchers seldom use deep learning theory, preferring instead to depend on tried-and-true techniques like image processing and classifiers. In this article, we recommend the CNN-LSTM and improved VGG-16 convolutional neural network for intra-frame forensic analysis of altered images.

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Shelar, Y. ., P. . Sharma, and C. S. D. . Rawat. “An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 3s, Mar. 2023, pp. 73-80,


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