Detecting Forged Images using Deep Learning
Pranav Sharma1, Pooja Santwani2, Rachit Narula3
1Pranav Sharma, B.Tech, Department of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore. (Tamil Nadu), India.
2Pooja Santwani, B.Tech, Department of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore. (Tamil Nadu), India.
3Rachit Narula, B.Tech, Department of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore. (Tamil Nadu), India.
Manuscript received on 18 August 2022 | Revised Manuscript received on 27 August 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 6-8 | Volume-12 Issue-1, October 2022. | Retrieval Number: 100.1/ijeat.A37921012122 | DOI: 10.35940/ijeat.A3792.1012122
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This availability and requirement of data calls for the credibility and authenticity of the data. One such domain is images where tampering creates concern , leading to wide spread of misinformation and fake news. Images are transferred to initiate propagandas on social handles and other platforms. Most of these images are tampered from the authentic original content to allude people and miscommunicate malicious information. In this application, our main work is to modify the existing MobileNetV2 family of neural networks to a more relevant version, so that we can identify and differentiate tampered images from authentic images. We will further create our own convolutional neural network, to create an application which can help us to identify and differentiate tampered images from authentic images and compare our model with MobileNetV2.
Keywords: Images, Application, MobileNetV2, Neural Network, Detecting, Deep Learning
Scope of the Article: Deep Learning