The current lexical substitution-based linguistic steganography primarily determines the substitutions by their linguistic suitability, overlooking the disparity in their capability against steganalysis. This oversight leads to the potential security risk by performing poor substitutions. To address this issue, this letter proposes aCausalPerceptionGuidedLinguisticSteganography(CPG-LS) via elaborate and secure lexical substitutions. CPG-LS constructs a causal perception network by making full use of a trained CNN discriminator to assess the security of each original word in the cover text and its substitutable candidates for controlling the embedding of secret message. Particularly, the causal score of each word is comprehensively measured from two perspectives by the causal perception network, one is word saliency, and the other is anti-steganalysis capability. Finally, the selection of original words is explicitly guided by their causal scores for securely embedding secret message. As the causal score of a word decreases, the perturbation caused by modifying this word becomes smaller, leading to stronger anti-steganalysis capability and lower semantic distortion in the obtained stegotexts. The experimental results demonstrate that CPG-LS achieves better text quality and anti-steganalysis capability than existing similar methods.

Abstract Deep learning approaches to interactive image segmentation are typically formulated as a binary labeling problem. A model trained to make predictions within a fixed set of labels (i.e., foreground and background labels) cannot be used to directly predict the binary masks of multiple objects of interest, which greatly limits its flexibility and adaptivity. The use of different classes of clicks as input is opted for and the first end‐to‐end learning model for multi‐object segmentation, based on a new designed neural network, is developed. The network consists of a visual feature extractor, a recurrent attention module and a dynamic segmentation head, extracts user click‐adapted appearance embedding features and spatial attention features, and then learns to transform this information into a segmentation of multiple objects. It is also proposed to train the network using a joint loss function, taking the embedding learning into account for segmentation. Comprehensive experiments are conducted on three benchmark datasets to demonstrate the effectiveness of the proposed method. It performs favorably against state‐of‐the‐art approaches on the multiple object segmentation task, for example, with 0.15 s per image, 0.06 s per object and mean IoU & F1 score of 84.90% on Pascal VOC 2012 validation set. It is further shown that the method can be used in numerous vision applications such as image recoloring and colorization.

We propose a novel optimization framework for video object segmentation, given the initial annotations of objects in the keyframes of an input video sequence. In this work, video data is represented by a Markov Random Field model, and segmentation is achieved by finding the minimum graph cut label assignment. More specifically, we first create a bilateral representation of the input video sequence which reduces the size of the graph that the min-cut must operate on. We then introduce dynamic appearance models to learn the segmentation likelihoods, and the reliability of likelihoods is measured to identify false likelihoods that may cause segmentation errors. Thus, the model accurately describes changes in the object's appearance that have evolved over time. Furthermore, we augment spatial and temporal connections using a soft higher-order potential, ensuring long-range label consistency in the segmentation. We provide extensive analysis and evaluation with respect to the influence of each component of the framework through the ablation study. Experiments on three benchmark datasets (DAVIS 2016, YouTube-Objects and SegTrack v2) show that our method achieves competitive performance compared to state-of-the-art while having the order of magnitude faster runtime.

We synthesize animations from a single image by transferring fluid motion of a video example globally. Given a target image of a fluid scene, an alpha matte is required to extract the fluid region. Our method needs to adjust a user-specified video example for producing the fluid motion suitable for the extracted fluid region. Employing the fluid video database, the flow field of the target image is obtained by warping the optical flow of a video frame that has a visually similar scene to the target image according to their scene correspondences, which assigns fluid orientation and speed automatically. Results show that our method is successful in preserving large fluid features in the synthesized animations. In comparison to existing approaches, it is both possible and useful to utilize our method to create flow animations with higher quality.