Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network

Shubham Anjankar, Somesh Telang, Khushalsingh Bharadwaj, R. Khandelwal
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Abstract

It might be challenging in the field of image forensics to identify the source camera of a picture. This researchproposes a noise adaptable convolutional neural network-based technique for camera identification. For cameraidentification, the suggested solution combines Photo Response Non-Uniformity (PRNU) noise and Noiseprint.Three parallel dimensionality expanded residual networks with convolutional layers of kernel size 1x1 were puttogether for enhanced feature extraction. The experiment mentioned above uses pictures from the ”Vision Dataset”as its subject matter. The experimental findings demonstrate the effectiveness of the suggested methodology inidentifying the source camera at the brand, model, and device levels. When two of the three networks were fedwith PRNU and one with noiseprint, the best performance was obtained.
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基于维数扩展残差网络的PRNU和噪声融合源相机识别
在图像取证领域,识别照片的源相机可能是一个挑战。本文提出了一种基于卷积神经网络的噪声自适应相机识别技术。对于相机识别,建议的解决方案结合了光响应非均匀性(PRNU)噪声和噪声打印。将核大小为1x1的卷积层的三个平行维扩展残差网络组合在一起,增强特征提取。上面提到的实验使用来自“视觉数据集”的图片作为其主题。实验结果证明了所建议的方法在品牌、型号和设备级别识别源相机方面的有效性。其中2个网络分别加入PRNU和1个网络分别加入噪声印迹,效果最佳。
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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