Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection, and image retouching detection are attracting significant attention from researchers. However, image editing techniques develop over time. An emerging image editing technique is colorization, in which grayscale images are colorized with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared with natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark, and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram-based fake colorized image detection and feature encoding-based fake colorized image detection. Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.

In this letter, we pioneer to propose a binarization embedded weakly-supervised video anomaly detection (BE-WSVAD) method by constructing a binarized GCN-based anomaly detection module. Compared to the existing weakly-supervised video anomaly detection (WS-VAD) methods, BE-WSVAD focuses on the detection efficiency, which is ignored by the existing literature yet vital in real applications. Specifically, to improve the detection performance of the binary anomaly detection module, we propose a binary network augmentation strategy in the training process. Due to the weakly supervision mechanism, the videos employed in the training process are usually lengthy, in which the lengthy-input dependencies tend to be exploited to improve the detection performance with extra memory consumption. Then, we propose the short-input inference modes, which can largely reduce the desired length of the input video. Experimental results demonstrate the superiority of our BE-WSVAD in terms of the memory and computational consumptions while giving comparable accuracies.

Although many graph convolutional neural networks (GCNNs) have achieved superior performances in semisupervised node classification, they are designed from either the spatial or spectral perspective, yet without a general theoretical basis. Besides, most of the existing GCNNs methods tend to ignore the ubiquitous noises in the network topology and node content and are thus unable to model these uncertainties. These drawbacks certainly reduce their effectiveness in integrating network topology and node content. To provide a probabilistic perspective to the GCNNs, we model the semisupervised node classification problem as a topology-constrained probabilistic latent space model, probabilistic graph convolutional network (PGCN). By representing the nodes in a more efficient distribution form, the proposed framework can seamlessly integrate the node content and network topology. When specifying the distribution in PGCN to be a Gaussian distribution, the transductive node classification problems can be solved by the general framework and a specific method, called PGCN with the Gaussian distribution representation (PGCN-G), is proposed. To overcome the overfitting problem in covariance estimation and reduce the computational complexity, PGCN-G is further improved to PGCN-G+ by imposing the covariance matrices of all vertices to possess the identical singular vectors. The optimization algorithm based on expectation-maximization indicates that the proposed method can iteratively denoise the network topology and node content with respect to each other. Besides the effectiveness of this top-down framework demonstrated via extensive experiments, it can also be deduced to cover the existing methods, graph convolutional network, graph attention network, and Gaussian mixture model and elaborate their characteristics and relationships by specific derivations.

Pedestrian attribute recognition, which can benefit other tasks such as person re-identification and pedestrian retrieval, is very important in video surveillance related tasks. In this paper, we observe that the existing methods tackle this problem from the perspective of multi-label classification without considering the hierarchical relationships among the attributes. In human cognition, the attributes can be categorized according to their semantic/abstraction levels. The high-level attributes can be predicted by reasoning from the low-level and medium-level attributes, while the recognition of the low-level and medium level attributes can be guided by the high-level attributes. Based on this attribute categorization, we propose a novel Hierarchical Reasoning Network (HR-Net), which can hierarchically predict the attributes at different abstraction levels in different stages of the network. We also propose an attribute reasoning structure to exploit the relationships among the attributes at different semantic levels. Experimental results demonstrate that the proposed network gives superior performances compared to the state-of-the-art techniques.

Graph Neural Networks (GNNs) have been generalized to process the heterogeneous graphs by various approaches. Unfortunately, these approaches usually model the heterogeneity via various complicated modules. This article aims to propose a simple yet effective framework to assign adequate ability to the homogeneous GNNs to handle the heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Network (RE-GNN), which employs only one parameter per relation to embed the importance of distinct types of relations and node-type-specific self-loop connections. To optimize these relation embeddings and the model parameters simultaneously, a gradient scaling factor is proposed to constrain the embeddings to converge to suitable values. Besides, we interpret the proposed RE-GNN from two perspectives, and theoretically demonstrate that our RE-GCN possesses more expressive power than GTN (which is a typical heterogeneous GNN, and it can generate meta-paths adaptively). Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.

In color image compression, the chroma components are often sub-sampled before compression and up-sampled after compression. Although sub-sampling the chroma components saves bit-cost for compression, it often induces extra color distortions in the compressed images. In this paper, we propose two approaches to tackle this problem. Firstly, we propose a subsampling method in the transform domain and apply it to both the chroma components. Then, based on this sub-sampling, we propose a novel method to modify the luma component. In our proposed luma modification algorithm, the distortions occurred on two chroma components can be coupled together and utilized to modify the luma component. With our proposed chroma subsampling and luma modification algorithms, we can achieve a low RGB distortion in practical image coding. Experimental results demonstrate that our proposed methods offer more significant coding gains compared with the state-of-the-art methods for the compression of color images.