Providing persuasive descriptions of points of interest (POI) is crucial for ensuring the quality of location-based services such as food delivery. However, informal textual descriptions by customers and unreliable geographic coordinates from indoor devices make it challenging to standardize both textual and geospatial descriptions of queried POIs. Previous works often take a retrieve-then-rank approach, which has limited feasibility in real-world delivery scenarios due to their dependency on a vast POI database and the extensive labor required for labeling. In this work, we propose the AMoS system, which is based on the observation that records referring to the same POI are either similar in both semantic and geospatial domains, or at least in one of them. The AMoS system leverages inherent semantic and geospatial similarities within historical POI records, combining them in graph-based clustering to retrieve candidates for standardizing a given query. We also propose a standardization paradigm that combines structured formatting rules of POI descriptions with content diversity. We evaluate the performance on 22356 orders from a real-world online delivery platform. Our retrieval performance outperforms baselines by 16% in precision and remains competitive compared to a supervised approach. Our standardization accuracy reaches 90.24%.

Screen-camera communication enables the seamless integration of encoded auxiliary information from the digital world into the physical domain—allowing users to obtain detailed information about an object of interest, such as a poster, simply by capturing a photo with a smartphone. Traditional screencamera communication methods, such as barcodes, occupy visual space and degrade aesthetics. While inter-frame encoding methods address these limitations, they are restricted to video streams or active screen displays. To enable content-preserving intraframe screen-camera communication, we propose InvisiCode, a noise-aware method for imperceptible, robust, and high-capacity encoding. We first quantitatively analyze screen-camera noise and identify predictable patterns in mid-high frequency Discrete Cosine Transform (DCT) coefficients, enabling mathematically bounded, noise-aware encoding. Based on this insight, we design an adaptive encoding algorithm that distributes data across multiple coefficients, balancing imperceptibility and resilience to noise. To ensure accurate decoding, we enhance$\mathrm{U}^{2}$-Net with Edge-Constraint Loss to improve boundary detection and precisely locate the encoded region in captured images. Experimental results demonstrate that InvisiCode is reliable and adaptable across various screen and camera configurations, including smartphones, tablets, laptops, and desktop monitors. It achieves a throughput of 784 bits per frame with a Bit Error Rate (BER) of less than 0.05, significantly outperforming previous methods. User studies confirm that the system introduces imperceptible distortion. Our code and demo are available at https://github.com/haikuoY/InvisiCode.

Machine learning models are increasingly recognized as valuable intellectual property (IP), prompting the development of a range of watermarking techniques aimed at safeguarding the IP of these models. However, in the context of federated learning (FL) models involving multiple owners, such as the participants in FL model training, conventional techniques designed for single-owner models prove ineffective due to limitations in their capacity and robustness. Few work has explored how to effectively embed watermarks to FL models for multiple-owners, which is non-trivial, especially when the number of owners is large. To fill this gap, we first analyze the capacity of existing watermarking methods. Second, we propose FedMark, a general large-capacity watermarking mechanism for FL, which leverages the Bloom Filter to achieve conflict-free watermarking of a large number of participants. Moreover, we propose a secret-sharing-based verification method to improve the watermarking robustness against false positives caused by Bloom Filter. Finally, comprehensive experiments show that our design can support over 150 participants to embed watermarks while the model accuracy varies within 1 %, and is robust to non-independent identical distributed data, different participant selection rates, model modifications, permutation attacks, scaling attacks and forging attacks.

With the rapid development of internet and computer technologies, video has become increasingly prevalent in information dissemination and daily communication. However, video data often contains a substantial amount of sensitive and private information, leading to security risks during its usage, such as privacy breaches, copyright infringements, and data theft. Current efforts in video encryption primarily focus on block-level or whole-frame-level encryption within video frames, which fails to achieve fine-grained privacy protection and lacks personalized access control. Moreover, many approaches also suffer from inadequate real-time performance and excessive storage overhead.To address these issues, we proposes a personalized pixel-level scheme for video privacy protection: PPL-enc. PPL-enc employs an instance segmentation model to delineate regions containing privacy information at the pixel level within video frames. Subsequently, we apply a highly real-time quadruple encryption algorithm to the pixel values within sensitive regions, which is performed before encoding and is robust to lossy compression. Different encryption keys utilize Attribute-Based Encryption (ABE) for personalized access control, allowing terminal users with different identities to access different content upon video decryption. We conduct extensive evaluations on the quality and efficiency of encryption and decryption, as well as security analysis, to demonstrate the efficiency of our proposed scheme. Experimental results show that the single-frame encryption overhead of PPL-enc is consistently less than 0.1s across videos of various resolutions, with the encrypted area having an average PSNR of 8.50 dB and an average SSIM of 0.079.

Online video-centric service is an emerging application paradigm that enables users to access personalized video services using public equipment. However, it also brings many privacy and security issues, since the online videos might be accessed by different users. To protect the video content privacy against untrusted recipients, we need to take fine-grained control of access permissions to video content. The same video content might be accessible to certain recipients while being restricted to others. Traditional methods generate and encode multiple redacted versions of the same video, leading to substantial increases in storage, processing, and communication costs, which is difficult in adapting to the demands of the ubiquitous multimedia era. Enabling cost-effective, personalized, and fine-grained access control for video content presents a significant challenge.Video inpainting methods have gained popularity for their notable ability to remove objects with plausible pixels. In this work, we introduce the Object-grained Video Inpainting (OVI) framework for personalized access control – objects in videos are accessible only to authorized users and are visually coherently blocked for all others. OVI is efficient irrespective of user count. We implement the prototype and evaluate its performance via security study, reconstruction effectiveness, and efficiency. The experimental results show that OVI speeds up video sharing and reduces communication savings by a Θ(n) factor over the baselines when there are n different accessing groups.

Obfuscation technologies have been well established for on-device image privacy protection, including pixelization, blurring, scribbling, sticker-covering, and inpainting. Despite their remarkable resistance to human observation, recent studies find that some of them are vulnerable to attacks by neural network-based recognition methods. In this work, we reveal the risk of privacy re-disclosure post image protection. Given an obfuscation-protected image, the privacy information includes 1) where the obfuscated region is and 2) what the hidden privacy-related objects are. Thus we focus on uncovering categories of privacy-related objects to evaluate the effectiveness of obfuscation technologies. Under severe obfuscation, unfortunately, even powerful object recognition models can hardly infer hidden privacy information.

We investigate coresets - succinct, small summaries of large data sets - so that solutions found on the summary are provably competitive with solution found on the full data set. We provide an overview over the state-of-the-art in coreset construction for machine learning. In Section 2, we present both the intuition behind and a theoretically sound framework to construct coresets for general problems and apply it to $k$-means clustering. In Section 3 we summarize existing coreset construction algorithms for a variety of machine learning problems such as maximum likelihood estimation of mixture models, Bayesian non-parametric models, principal component analysis, regression and general empirical risk minimization.