Determination of image authenticity usually requires the identification and localization of the manipulated regions of images. Hence, image manipulation detection has become one of the most important tasks in the field of multimedia forensics. Recently, Convolutional Neural Networks (CNNs) have achieved promising performance in image manipulation detection. However, it is hard for the existing CNN-based manipulation detection approaches to accurately identify and localize the manipulated regions that have undergone geometric transformations, since CNNs are limited by their inability to be geometrically invariant. To address this issue, we propose a geometric rectification-based neural network architecture for image manipulation detection. In this type of network architecture, following the detection of a set of potential manipulated regions (PMRs) using Region Proposal Network, the Spatial Transformer Network is employed to geometrically rectify the convolutional feature maps (CFMs) of these regions to obtain the geometrically rectified CFMs (GR-CFMs). Subsequently, the residual feature maps (RFMs) are computed to capture the characteristic inconsistency between the CFMs and GR-CFMs of each PMR. Finally, the computed RFMs are automatically integrated with the GR-CFMs by a designed attention module to determine whether each PMR is a manipulated region and to localize the manipulated part at the pixel-level. Extensive experiments on the public data set as well as on our challenging data set demonstrate that the proposed network architecture achieves desirable performance in identifying and localizing regions with common tampering artifacts, which involve geometric transformations.

An echo state network (z) can provide an efficient dynamic solution for predicting time series problems. However, in most cases, ESN models are applied for predictions rather than classifications. The applications of ESN in time series classification (TSC) problems have yet to be fully studied. Moreover, the conventional randomly generated ESN is unlikely to be optimal because of the randomly generated input and reservoir weights, which are not always guaranteed to be optimal. Randomly generating all layer weights is improper, because a purely random layer might destroy the useful features. To overcome this disadvantage, this study provides a new input weight establishment framework of ESN based on autoencoder (AE) theory for TSC tasks. A global reversible AE (GRAE) algorithm is proposed to reestablish the random initialization input weights of the ESN. In existing ESN-AEs, the output weights obtained in the encoding process are directly reused as the initial input weights. By contrast, in GRAE, the reservoir layer with a reversible activation function is calculated by pulling the decoding layer output back and injecting it into the reservoir layer. Thus, feature learning is enriched by additional information, which results in improved performance. The current weights of the encoding layer are iteratively replaced by the decoding layer to ensure that the outputs of the GRAE are remarkably correlated with the input data. Visualization analyses and experiments of the input weights on a massive set of UCR time series datasets indicate that the proposed GRAE method can considerably improve the original two-layer ESN-based classifiers and the proposed GRAE-ESN classifier yields better performance compared with traditional state-of-the-art TSC classifiers. Furthermore, the proposed method can provide comparable performance and considerably faster training speed compared with three deep learning classifiers.