# Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators
## 1 Introduction
[PERSON] and [PERSON] [15] proposed a model using CNN and multilayer perceptron (MLP) for high-pass filtering, low-pass filtering, de-noising, and tonal adjustment that has four types of operations. Eight convolutional layers are utilized in the proposed CNN. The MLP classifies the images using moments that are extracted in the last part of the CNN model. The aforementioned method is also compared with the manual feature extraction method. The experiments are discussed for 512 \(\times\) 512 size images only. [PERSON] et al. [16; 17] utilized the Siamese network for pair-wise learning. Two identical networks are used to classify multiple operations--median filtering, Gaussian filtering, gamma correction, additive white Gaussian noise, and image resizing. The authors claim that the proposed method can detect a processed image for an unseen operation that is not included in the training. The choice of a two-stream CNN architecture gives better performance than analogous single-stream architecture [13]. However, the model capability decreases on unknown data sets abruptly. [PERSON] et al. [18] discussed the densely connected CNN model for detecting eleven types of operations. Each dense block is followed by a transition layer and pooling layer. The transition layer performs 1 \(\times\) 1 convolutions to reduce the number of feature maps and computation costs. The dataset of training and testing is the same which can make unbiased performance evaluation difficult. [PERSON] et al. [19] applied the Siamese network for identifying some operations like as inclusion of text, logo, and black block in the image. Operations also include image resampling, Gaussian noise, and Gamma correction. The Siamese network utilized the AlexNet and ResNet-18. Uncompressed images are considered only in experiments. [PERSON] et al. [20] introduced a CNN model with two convolutional layers only to detect seven types of operations. DCT coefficients of median filter residual are used as input in the CNN network. Large-size filters are used in the convolutional layer. [PERSON] et al. [21] detected image operations like median filtering, image resizing, and histogram equalization. The features are extracted using two neural networks [12; 29]. A random feature selection approach is utilized to select the robust features from the CNN network, and an SVM classifier is applied to find the type of attack finally. Detection of image operations order is also a great concern for a deep understanding of image processing history. Some efforts [22; 23; 24; 25; 26] are performed to detect the order of operations. In [22; 23], an analysis is given to find the reasons for the non-detection of operator sequence order, where a framework based on mutual information is suggested. The methods are not able to detect some operator sequences. JPEG compressed images are not detectable using the previous model. [PERSON] [24] discussed the theoretical possibilities of operator order detection. [PERSON] and [PERSON] [25] discussed a CNN with a constrained convolution layer for estimating the order of operations. [PERSON] et al. [26] suggested two-stream CNN, to find out the operators and their respective orders. Though a customized preprocessing is required to apply for each operation, the method can detect operation even for an unknown parameter on the same operation using weight transfer. In this paper, a non-reducing convolutional neural network is proposed that can assure the maximal flow of details between layers. The specific contributions of the proposed network can be outlined as follows:  
* The proposed non-reducing CNN can detect a dual-operated image and the operation sequence. Different types of operations such as median filtering, Gaussian blurring, image resizing, and un-sharp masking are detected successfully. * Multiple convolutional layers are inserted in the CNN network by adopting a bottleneck approach in the proposed method. The computational requirement of the proposed CNN is less due to fewer learning parameters.