利用深度学习检测伪造图像

Pranav Sharma, Pooja Santwani, Rachit Narula
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引用次数: 0

摘要

这种数据的可用性和要求要求数据的可信性和真实性。其中一个领域是图像,篡改会引起关注,导致错误信息和假新闻的广泛传播。图片被转移到社交账号和其他平台上进行宣传。这些图片大多是从真实的原始内容中篡改出来的,用来影射人,误传恶意信息。在这个应用中,我们的主要工作是将现有的MobileNetV2系列神经网络修改为一个更相关的版本,这样我们就可以识别和区分篡改图像和真实图像。我们将进一步创建我们自己的卷积神经网络,创建一个应用程序,可以帮助我们识别和区分篡改图像和真实图像,并将我们的模型与MobileNetV2进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Forged Images using Deep Learning
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.
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