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Semi-quantum key distribution (SQKD) allows sharing random keys between a quantum user and a classical user. However, implementing classical user operations is challenging, posing a hurdle to achieving the Single-state protocol. By using the "selective modulation" method, the feasibility of SQKD is verified in principle. The proposal of the selective modulation method enables the realization of other protocols for SQKD. To advance experimental progress in SQKD, we propose and implement a phase-encoded semi-quantum key distribution system based on the Single-state protocol and the "selective modulation" method. The system operates at a frequency of 100MHz and an average photon number of 0.1. The interference contrast achieved 96.52%, the average quantum bit error rate was 1.19%, and the raw key rate reached 88Kbps. Our experimental results demonstrate the feasibility and stability of the proposed phase-encoded semi-quantum key distribution system. Furthermore, by leveraging the "selective modulation" scheme proposed in this paper, we develop a comprehensive theoretical description of selective modulation. Through an analysis of quantum state evolution, we assess the security of our system, ultimately demonstrating its resilience against attacks targeting quantum states. The classical user of our system requires only two optical devices, significantly reducing the equipment requirements and enhancing its application potential. This work validates the feasibility of semi-quantum key distribution experiments and provides ideas for future research on semi-quantum key distribution experiments and security studies.

Eavesdropping from the user's smartphone is a well-known threat to the user's safety and privacy. Existing studies show that loudspeaker reverberation can inject speech into motion sensor readings, leading to speech eavesdropping. While more devastating attacks on ear speakers, which produce much smaller scale vibrations, were believed impossible to eavesdrop with zero-permission motion sensors. In this work, we revisit this important line of reach. We explore recent trends in smartphone manufacturers that include extra/powerful speakers in place of small ear speakers, and demonstrate the feasibility of using motion sensors to capture such tiny speech vibrations. We investigate the impacts of these new ear speakers on built-in motion sensors and examine the potential to elicit private speech information from the minute vibrations. Our designed system EarSpy can successfully detect word regions, time, and frequency domain features and generate a spectrogram for each word region. We train and test the extracted data using classical machine learning algorithms and convolutional neural networks. We found up to 98.66% accuracy in gender detection, 92.6% detection in speaker detection, and 56.42% detection in digit detection (which is 5X more significant than the random selection (10%)). Our result unveils the potential threat of eavesdropping on phone conversations from ear speakers using motion sensors.

Nowadays, traditional authentication methods are vulnerable to face attacks that are often based on inherent security issues. Professional attackers leverage adversarial offenses on the security holes. Biometrics has intrinsic advantages to overcome the traditional authentication methods on security, success rates, efficiency, and accessibility. Biometrics has wide prospects to implement various applications in fields. Whether in authentication security or clinical medicine, biometrics is one of the mainstream studies. In this paper, we surveyed and reviewed some related studies of biometrics, which are outstanding and significant in driving the development and popularization of biometrics. Although they still have some inherent disadvantages to restrict popularization, these obstacles could not conceal the promising future of biometrics. Multi-factors continuous biometrics authentication has become the mainstream trend of development. We reflect the findings as well as the challenges of the studies in the survey paper., Comment: 6 pages, 9 figures, 9 references

Recently backdoor attack has become an emerging threat to the security of deep neural network (DNN) models. To date, most of the existing studies focus on backdoor attack against the uncompressed model; while the vulnerability of compressed DNNs, which are widely used in the practical applications, is little exploited yet. In this paper, we propose to study and develop Robust and Imperceptible Backdoor Attack against Compact DNN models (RIBAC). By performing systematic analysis and exploration on the important design knobs, we propose a framework that can learn the proper trigger patterns, model parameters and pruning masks in an efficient way. Thereby achieving high trigger stealthiness, high attack success rate and high model efficiency simultaneously. Extensive evaluations across different datasets, including the test against the state-of-the-art defense mechanisms, demonstrate the high robustness, stealthiness and model efficiency of RIBAC. Code is available at https://github.com/huyvnphan/ECCV2022-RIBAC, Comment: Code is available at https://github.com/huyvnphan/ECCV2022-RIBAC

Graphical User Interface (GUI) is ubiquitous in almost all modern desktop software, mobile applications, and online websites. A good GUI design is crucial to the success of the software in the market, but designing a good GUI which requires much innovation and creativity is difficult even to well-trained designers. Besides, the requirement of the rapid development of GUI design also aggravates designers' working load. So, the availability of various automated generated GUIs can help enhance the design personalization and specialization as they can cater to the taste of different designers. To assist designers, we develop a model GUIGAN to automatically generate GUI designs. Different from conventional image generation models based on image pixels, our GUIGAN is to reuse GUI components collected from existing mobile app GUIs for composing a new design that is similar to natural-language generation. Our GUIGAN is based on SeqGAN by modeling the GUI component style compatibility and GUI structure. The evaluation demonstrates that our model significantly outperforms the best of the baseline methods by 30.77% in Frechet Inception distance (FID) and 12.35% in 1-Nearest Neighbor Accuracy (1-NNA). Through a pilot user study, we provide initial evidence of the usefulness of our approach for generating acceptable brand new GUI designs., Comment: 13 pages, 10 figures, accepted for publication at ICSE2021 Technical Track

Photons with a helical phase front (twisted photons) can carry a discrete, in principle, unbounded amount of orbital angular momentum (OAM). Twisted single-photons have been demonstrated as a high-dimensional quantum system with information processing ability far beyond the widely used two-level qubits. To date, the generations of single-photons carrying OAM merely rely on the non-linear process in bulk crystals, e.g., spontaneous parametric down-conversion (SPDC), which unavoidably limits both the efficiency and the scalability of the source. Therefore, an on-demand OAM quantum light source on a semiconductor chip is yet illusive and highly desirable for integrated photonic quantum technologies. Here we demonstrate highly-efficient emission of twisted single-photons from solid-state quantum emitters embedded in a microring with angular gratings. The cavity QED effect allows the generations of single-photons and encoding OAM in the same nanostructure and therefore enables the realization of devices with very small footprints and great scalability. The OAM states of singe-photons are clearly identified via quantum interference of single-photons with themselves. Our device may boost the development of integrated quantum photonic devices with potential applications towards high-dimensional quantum information processing., Comment: Supplementary information is available upon requests