Tyagi Shobhit, Yadav Divakar
Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, India.
J Forensic Sci. 2023 Mar;68(2):461-469. doi: 10.1111/1556-4029.15210. Epub 2023 Jan 31.
The advancements in Image editing techniques produce realistic-looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth-wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth-wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state-of-the-art methods by a large margin.
图像编辑技术的进步能够轻松生成看起来逼真的人工图像。这些图像能够轻易绕过取证系统，使得认证过程变得更加繁琐和困难。为了克服这个问题，我们引入了一种名为法医网络（ForensicNet）的现代卷积神经网络（CNN），其灵感来源于计算机视觉领域的最新进展。我们的卷积神经网络有三个主要贡献：倒置瓶颈、单独的下采样层以及使用深度卷积在空间维度上混合信息。倒置瓶颈有助于提高准确率并减少网络参数/浮点运算次数（FLOPs）。单独的下采样层有助于网络收敛。每当空间分辨率改变时，归一化层也有助于稳定训练。深度卷积是一种分组卷积，其中组数和通道数相同。实验表明，法医网络在很大程度上优于当前的先进方法。
