Ensemble Reversible Data Hiding

The conventional reversible data hiding (RDH) algorithms often consider the host as a whole to embed a secret payload. In order to achieve satisfactory rate-distortion performance, the secret bits are embedded into the noise-like component of the host such as prediction errors. From the rate-distortion optimization view, it may be not optimal since the data embedding units use the identical parameters. This motivates us to present a segmented data embedding strategy for efficient RDH in this paper, in which the raw host could be partitioned into multiple subhosts such that each one can freely optimize and use the data embedding parameters. Moreover, it enables us to apply different RDH algorithms within different subhosts, which is defined as ensemble. Notice that, the ensemble defined here is different from that in machine learning. Accordingly, the conventional operation corresponds to a special case of the proposed work. Since it is a general strategy, we combine some state-of-the-art algorithms to construct a new system using the proposed embedding strategy to evaluate the rate-distortion performance. Experimental results have shown that, the ensemble RDH system could outperform the original versions in most cases, which has shown the superiority and applicability.Comment: Fig. 1 was updated due to a minor erro

PIANO: Proximity-based User Authentication on Voice-Powered Internet-of-Things Devices

Voice is envisioned to be a popular way for humans to interact with Internet-of-Things (IoT) devices. We propose a proximity-based user authentication method (called PIANO) for access control on such voice-powered IoT devices. PIANO leverages the built-in speaker, microphone, and Bluetooth that voice-powered IoT devices often already have. Specifically, we assume that a user carries a personal voice-powered device (e.g., smartphone, smartwatch, or smartglass), which serves as the user's identity. When another voice-powered IoT device of the user requires authentication, PIANO estimates the distance between the two devices by playing and detecting certain acoustic signals; PIANO grants access if the estimated distance is no larger than a user-selected threshold. We implemented a proof-of-concept prototype of PIANO. Through theoretical and empirical evaluations, we find that PIANO is secure, reliable, personalizable, and efficient.Comment: To appear in ICDCS'1

Extinction Pattern of Reef Ecosystems in Latest Permian

Studies of two Permian-Triassic boundary (PTB) sections on top of a Changhsingian reef in Ziyun, Guizhou Province, southwestern China indicate that the end-Permian mass extinction of reef ecosystems occurred in two steps. The first step is the extinction of all stenotropic organisms such as calcisponges and fusulinids in the latest Permian (in the Clarkina yini conodont zone). The biota after the first extinction is simple, comprising eurytropic organisms including microgastropods, ostracods, and some small burrowing organisms, or only algal mats. At the beginning of the Early Triassic (i.e. the beginning of the Hindeodus parvus zone), the environments became anoxic, and the microgastropod dominated biota or algal mats disappeared, which constituted the second episode of the mass extinction. The biota after the second extinction comprises small spherical microproblematica, some kinds of specialized organisms tolerant of anoxic or oxygen-poor conditions. As the environments became oxygenated, the specialized biota was replaced by a microgastropod-dominated simple biota. When the environmental conditions improved further, the simple biota was replaced by a diverse biota with normal-sized ammonoids, bivalves, and gastropods, representing restoration of normal oceanic conditions. Comparison with PTB sections in Dolomites, Italy and Meishan, Zhejiang Province shows that non-reef ecosystems had a similar first episode of mass extinction in the latest Permian. In the case that oceanic anoxia happened, non-reef ecosystems had a second extinction episode similar to that of reef ecosystems