Detection of Image Tampering Using Deep Learning, Error Levels and Noise Residuals

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sunen Chakraborty, Kingshuk Chatterjee, Paramita Dey
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引用次数: 0

Abstract

Images once were considered a reliable source of information. However, when photo-editing software started to get noticed it gave rise to illegal activities which is called image tampering. These days we can come across innumerable tampered images across the internet. Software such as Photoshop, GNU Image Manipulation Program, etc. are applied to form tampered images from real ones in just a few minutes. To discover hidden signs of tampering in an image deep learning models are an effective tool than any other methods. Models used in deep learning are capable of extracting intricate features from an image automatically. Here we proposed a combination of traditional handcrafted features along with a deep learning model to differentiate between authentic and tampered images. We have presented a dual-branch Convolutional Neural Network in conjunction with Error Level Analysis and noise residuals from Spatial Rich Model. For our experiment, we utilized the freely accessible CASIA dataset. After training the dual-branch network for 16 epochs, it generated an accuracy of 98.55%. We have also provided a comparative analysis with other previously proposed work in the field of image forgery detection. This hybrid approach proves that deep learning models along with some well-known traditional approaches can provide better results for detecting tampered images.

Abstract Image

利用深度学习、误差水平和噪声残差检测图像篡改
图片曾一度被认为是可靠的信息来源。然而,当图片编辑软件开始受到关注时,非法活动也随之产生,这就是所谓的篡改图片。如今,我们可以在互联网上看到无数被篡改的图片。Photoshop、GNU Image Manipulation Program 等软件可在几分钟内将真实图像篡改成篡改图像。要发现图像中隐藏的篡改痕迹,深度学习模型是比其他方法更有效的工具。深度学习中使用的模型能够自动从图像中提取复杂的特征。在这里,我们建议将传统的手工特征与深度学习模型相结合,以区分真实图像和篡改图像。我们将双分支卷积神经网络与误差水平分析和空间富模型的噪声残差相结合。在实验中,我们使用了可免费访问的 CASIA 数据集。在对双分支网络进行了 16 次历时训练后,其准确率达到了 98.55%。我们还与之前在图像伪造检测领域提出的其他工作进行了对比分析。这种混合方法证明,深度学习模型和一些著名的传统方法可以为检测篡改图像提供更好的结果。
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来源期刊
Neural Processing Letters
Neural Processing Letters 工程技术-计算机:人工智能
CiteScore
4.90
自引率
12.90%
发文量
392
审稿时长
2.8 months
期刊介绍: Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters
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