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Semantic communication (SemCom) enhances transmission efficiency by sending only task-relevant information compared to traditional methods. However, transmitting semantic-rich data over insecure or public channels poses security and privacy risks. This paper addresses the privacy problem of transmitting images over wiretap channels and proposes a novel SemCom approach ensuring privacy through a differential privacy (DP)-based image protection and deprotection mechanism. The method utilizes the GAN inversion technique to extract disentangled semantic features and applies a DP mechanism to protect sensitive features within the extracted semantic information. To address the non-invertibility of DP, we introduce two neural networks to approximate the DP application and removal processes, offering a privacy protection level close to that by the original DP process. Simulation results validate the effectiveness of our method in preventing eavesdroppers from obtaining sensitive information while maintaining high-fidelity image reconstruction at the legitimate receiver.

This paper addresses the challenge of achieving information-theoretic security in semantic communication (SeCom) over a wiretap channel, where a legitimate receiver coexists with an eavesdropper experiencing a poorer channel condition. Despite previous efforts to secure SeCom against eavesdroppers, achieving information-theoretic security in such schemes remains an open issue. In this work, we propose a secure digital SeCom approach based on superposition codes, aiming to attain nearly information-theoretic security. Our proposed method involves associating semantic information with satellite constellation points within a double-layered constellation map, where cloud center constellation points are randomly selected. By carefully allocating power between these two layers of constellation, we ensure that the symbol error probability (SEP) of the eavesdropper decoding satellite constellation points is nearly equivalent to random guessing, while maintaining a low SEP for the legitimate receiver to successfully decode the semantic information. Simulation results showcase that the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) for the eavesdropper's reconstructed data, using our proposed method, can range from decoding Gaussian-distributed random noise to approaching the variance of the data. This validates the ability of our method to achieve nearly information-theoretic security, demonstrating superior data security compared to benchmark methods., Comment: 13 pages, 16 figures

Adaptive rate control for deep joint source and channel coding (JSCC) is considered as an effective approach to transmit sufficient information in scenarios with limited communication resources. We propose a deep JSCC scheme for wireless image transmission with entropy-aware adaptive rate control, using a single deep neural network to support multiple rates and automatically adjust the rate based on the feature maps of the input image and their entropy, as well as the channel conditions. In particular, we maximize the entropy of the feature maps to increase the average information carried by each transmitted symbol during the training. We further decide which feature maps should be activated based on their entropy, which improves the efficiency of the transmitted symbols. We also propose a pruning module to remove less important pixels in the activated feature maps in order to further improve transmission efficiency. The experimental results demonstrate that our proposed scheme learns an effective rate control strategy that reduces the required channel bandwidth while preserving the quality of the reconstructed images.

How can we effectively utilise the 2D monocular image information for recovering the 6D pose (6-DoF) of the visual objects? Deep learning has shown to be effective for robust and real-time monocular pose estimation. Oftentimes, the network learns to regress the 6-DoF pose using a naive loss function. However, due to a lack of geometrical scene understanding from the directly regressed pose estimation, there are misalignments between the rendered mesh from the 3D object and the 2D instance segmentation result, e.g., bounding boxes and masks prediction. This paper bridges the gap between 2D mask generation and 3D location prediction via a differentiable neural mesh renderer. We utilise the overlay between the accurate mask prediction and less accurate mesh prediction to iteratively optimise the direct regressed 6D pose information with a focus on translation estimation. By leveraging geometry, we demonstrate that our technique significantly improves direct regression performance on the difficult task of translation estimation and achieve the state of the art results on Peking University/Baidu - Autonomous Driving dataset and the ApolloScape 3D Car Instance dataset. The code can be found at \url{https://bit.ly/2IRihfU}.

Many important physical phenomena involve subtle signals that are difficult to observe with the unaided eye, yet visualizing them can be very informative. Current motion magnification techniques can reveal these small temporal variations in video, but require precise prior knowledge about the target signal, and cannot deal with interference motions at a similar frequency. We present DeepMag an end-to-end deep neural video-processing framework based on gradient ascent that enables automated magnification of subtle color and motion signals from a specific source, even in the presence of large motions of various velocities. While the approach is generalizable, the advantages of DeepMag are highlighted via the task of video-based physiological visualization. Through systematic quantitative and qualitative evaluation of the approach on videos with different levels of head motion, we compare the magnification of pulse and respiration to existing state-of-the-art methods. Our method produces magnified videos with substantially fewer artifacts and blurring whilst magnifying the physiological changes by a similar degree., Comment: 24 pages, 13 figures

Objective: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices. However, most of the approaches work only in bright environments in which subtle photoplethysmographic and ballistocardiographic signals can be easily analyzed and/or require expensive and custom hardware to perform the measurements. Approach: This work introduces a low-cost method to measure subtle motions associated with the carotid pulse and breathing movement from the neck using near-infrared (NIR) video imaging. A skin reflection model of the neck was established to provide a theoretical foundation for the method. In particular, the method relies on template matching for neck detection, Principal Component Analysis for feature extraction, and Hidden Markov Models for data smoothing. Main Results: We compared the estimated HR and BR measures with ones provided by an FDA-cleared device in a 12-participant laboratory study: the estimates achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per minute under both bright and dark lighting. Significance: This work advances the possibilities of non-contact physiological measurement in real-life conditions in which environmental illumination is limited and in which the face of the person is not readily available or needs to be protected. Due to the increasing availability of NIR imaging devices, the described methods are readily scalable., Comment: 21 pages, 15 figures

Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for video-based measurement of heart and breathing rate using a deep convolutional network. The system features a new motion representation based on a skin reflection model and a new attention mechanism using appearance information to guide motion estimation, both of which enable robust measurement under heterogeneous lighting and major motions. Our approach significantly outperforms all current state-of-the-art methods on both RGB and infrared video datasets. Furthermore, it allows spatial-temporal distributions of physiological signals to be visualized via the attention mechanism., Comment: Accepted paper at ECCV 2018. 16 pages, 3 figures, supplementary materials in the ancillary files

In this paper we present Organic Primitives, an enabling toolbox that expands upon the library of input-output devices in HCI and facilitates the design of interactions with organic, fluid-based systems. We formulated color, odor and shape changing material primitives which act as sensor-actuators that convert pH signals into human-readable outputs. Food-grade organic molecules anthocyanin, vanillin, and chitosan were employed as dopants to synthesize materials which output a spectrum of colors, degrees of shape deformation, and switch between odorous and non-odorous states. We evaluated the individual output properties of our sensor-actuators to assess the rate, range, and reversibility of the changes as a function of pH 2-10. We present a design space with techniques for enhancing the functionality of the material primitives, and offer passive and computational methods for controlling the material interfaces. Finally, we explore applications enabled by Organic Primitives under four contexts: environmental, cosmetic, edible, and interspecies., Comment: Updated paper