Existing methods consider Word Segmentation (WS) as sequence tagging. Each tag indicates the position of the current character in a segment. The exactness of the position for any non-boundaries character is unnecessary. Any incorrect inner prediction reduces model performance. The position information restricts tag-to-tag transition. Thereby, extra context information and the Conditional Random Field (CRF) network are desired to control unreasonable tag transition. To steer away from the implicit restriction, we propose the Separation(Sp)-Adhesion(Ad), which targets straight on the essential character-to-character connections, to tackle the WS task directly. Merely bigram that is specially tailored for “Sp-Ad” is required and considered as the processing unit to identify the connection states of every two adjacent characters. The elimination of the position restriction makes the model independent of the CRF layer which is widely adopted to revise unreasonable tags. Therefore, CRF can then be substituted with a classification network. We construct the Separation Inference (SpIn) framework based on the bigram features and softmax classification network to tackle the WS task. SpIn significantly reduces the inference complexity, dispels extra context information, and boosts the accuracy of the WS task. Besides its effectiveness in Chinese Word Segmentation, performance boosts on Japanese and Korean Word Segmentation further prove SpIn is universal for East Asian Languages. Moreover, our extensive experiments also verify the cross-domain effectiveness of SpIn by attaining state-of-the-art performances in the benchmark tests of in-domain and cross-domain Chinese Word Segmentation.

With the prevailing of live video streaming, establishing an online pixelation method for privacy-sensitive objects is an urgency. Caused by the inaccurate detection of privacy-sensitive objects, simply migrating the tracking-by-detection structure applied in offline pixelation into the online form will incur problems in target initialization, drifting, and over-pixelation. To cope with the inevitable but impacting detection issue, we propose a novel Privacy-sensitive Objects Pixelation (PsOP) framework for automatic personal privacy filtering during live video streaming. Leveraging pre-trained detection networks, our PsOP is extendable to any potential privacy-sensitive objects pixelation. Employing the embedding networks and the proposed Positioned Incremental Affinity Propagation (PIAP) clustering algorithm as the backbone, our PsOP unifies the pixelation of discriminating and indiscriminating pixelation objects through trajectories generation. In addition to the pixelation accuracy boosting, experiment results on the streaming video data we built show that the proposed PsOP can significantly reduce the over-pixelation ratio in privacy-sensitive object pixelation.

A larger portion of fake news quotes untampered images from other sources with ulterior motives rather than conducting image forgery. Such elaborate engraftments keep the inconsistency between images and text reports stealthy, thereby, palm off the spurious for the genuine. This paper proposes an architecture named News Image Steganography (NIS) to reveal the aforementioned inconsistency through image steganography based on GAN. Extractive summarization about a news image is generated based on its source texts, and a learned steganographic algorithm encodes and decodes the summarization of the image in a manner that approaches perceptual invisibility. Once an encoded image is quoted, its source summarization can be decoded and further presented as the ground truth to verify the quoting news. The pairwise encoder and decoder endow images of the capability to carry along their imperceptible summarization. Our NIS reveals the underlying inconsistency, thereby, according to our experiments and investigations, contributes to the identification accuracy of fake news that engrafts untampered images.