Nowadays, many users make money by publishing content on social media platforms. In order to attract users’ attention, they often take measures to promote themselves. The security vulnerabilities in social media platforms may provide convenience for their user promotion work. We call this type of vulnerability the user promotion security vulnerability (UPSV). UPSV may cause unfair competition and endanger the interests of legitimate users and the social media platforms. Therefore it has great research significance to find and fix this vulnerability. In this paper, we propose a UPSV which widely exists in the function of sending messages to strangers of in-app chatting of many social media platforms. We first analyzed this vulnerability in some apps, and then YY app (China’s largest live streaming platform) was chosen as the research object to verify the actual effect of the vulnerability on illegitimate user promotion. We took the method of promoting a target YY streamer through sending promotional links to viewers, and to improve promotion effect, we used the method of user preference learning to select viewers for promotion. The experimental results show that among the promoted viewers, more than 44% entered the target streamers’ channels to watch live streaming, more than 21% followed the target steamers, and more than 13% gave gifts to the target steamers. It fully proves that this UPSV is real, exploitable and harmful, and we also proposed some fix suggestions to help the platforms to fix it.

The accurate and efficient detection of steel surface defects remains challenging due to complex backgrounds, diverse defect types, and varying defect scales. The existing CNN-based methods often struggle with capturing long-range dependencies and handling complex background noise, resulting in suboptimal performance. Meanwhile, although Transformer-based approaches are effective in modeling global context, they typically require large-scale datasets and are computationally expensive, limiting their practicality for industrial applications. To address these challenges, we introduce a novel attention-based salient object detector, called the ASOD, to enhance the effectiveness of detectors for strip steel surface defects. In particular, we first design a novel channel-attention-based block including global max/average pooling to focus on the relevant channel-wise features while suppressing irrelevant channel responses, where maximizing pooling extracts the main features of local regions, while removing irrelevant features and average pooling obtain the overall features while removing local details. Then, a new block based on spatial attention is designed to emphasize the area with strip steel surface defects while suppressing irrelevant background areas. In addition, a new cross-spatial-attention-based block is designed to fuse the feature maps with multiple scales filtered through the proposed channel and spatial attention to produce features with better semantic and spatial information such that the detector adapts to strip steel defects of multiple sizes. The experiments show that the ASOD achieves superior performance across multiple evaluation metrics, with a weighted F-measure of 0.9559, an structure measure of 0.9230, a Pratt’s figure of meri of 0.0113, and an mean absolute error of 0.0144. In addition, the ASOD demonstrates strong robustness to noise interference, maintaining consistently high performance even with 10–20% dataset noise, which confirms its stability and reliability.

Accurate identification of surface defects on steel strips is critical for manufacturing quality assurance and operational reliability. Although deep learning has greatly advanced defect detection, precise recognition remains challenging due to significant background texture interference, loss of spatial details, and semantic imbalance across multiscale features. To address these challenges, we propose A²S²C-Det, a novel detector that integrates dual-path adaptive aggregation with spatial–semantic compensation to enhance feature representation for defect detection. First, we design a plug-and-play semantic refinement bottleneck (SRB) that augments backbone features through multiscale perception and a feature-screening bottleneck, enabling the model to suppress background interference while capturing subtle defect shapes. We further introduce a dual-path adaptive aggregation (DPAA) module that fuses complementary information from cross-level semantic consistency and fine-grained structural cues via two coordinated pathways, alleviating semantic imbalance across scales. Finally, we develop a spatial-semantic gated compensation (SSGC) module that adaptively supplies semantic information to low-level features while delivering spatial details to high-level features, recovering lost spatial details in high-level features. Extensive experiments on three benchmark datasets demonstrate that our A²S²C-Det achieves mAP₅₀ of 82.0%, 73.0%, and 91.2%, and mAP of 47.1%, 36.5%, and 60.6%, respectively, comparing favorably against current state-of-the-art methods.

Skin lesion segmentation has achieved notable progress in recent years; however, accurate delineation remains challenging due to complex backgrounds, ambiguous boundaries, and low lesion-to-skin contrast. To address these issues, we propose the lesion-aware triple-path feature fusion network (LTPNet), an end-to-end framework that progressively processes features through extraction, refinement, and aggregation stages. In the extraction stage, we incorporate a general foreground–background attention to suppress background interference and accelerate model convergence. In the refinement stage, we introduce an attentive spatial modulator (ASM) to jointly exploit local structural cues and global semantic context for precise spatial modulation. We further develop a lesion-aware lite-gate attention (LALGA) module that performs local spatial feature modulation and global channel recalibration tailored to lesion characteristics. In the aggregation stage, we propose a triple-path feature fusion (TPFF) module that explicitly models feature relationships across scales via three complementary pathways: a common path (CP) for semantic consistency, a saliency path (SP) for highlighting co-activated regions, and a difference path (DP) for accentuating structural discrepancies. Extensive experiments on in-domain and cross-domain datasets show that LTPNet achieves superior segmentation accuracy with reasonable inference efficiency and model complexity, demonstrating its potential for efficient and reliable clinical decision support.

Geographical location and mobile phone numbers are important parts of user privacy and lots of studies have been working on the privacy leakage problems of these two aspects. However, no researchers have ever studied the security problems that can be caused by the interaction between them. We show a new form of network attack in this paper by making full use of the relationship between them; namely, we try to break a user’s mobile phone number with the aid of a user’s geographical location that has been broken. We study the phenomenon of exposing a user’s geographical location and parts of their phone number that exist in a series of popular software products, and the possibility of the user’s mobile phone number to be broken is discussed. We choose one of the software (the largest entertainment webcast platform in China—YY) as the research object. First, taking advantage of a series of security vulnerabilities that exist in YY, a user’s accurate geographical location is broken by the trilateration localization algorithm. Then, their mobile phone number attribution can be inferred according to their geographical location. Next, a mobile phone number test set is constructed according to the mobile phone segment allocation made by the three carriers (telecommunication operators) and the exposed parts of the user’s phone number. Finally, a brute-force method is used to break the user’s mobile phone number. The great effect of a user’s geographical location on breaking a mobile phone number is proved by experiments, and security precaution suggestions are given at the end of the paper.

Photovoltaic (PV) cell defects, such as cracks and star cracks, often arise during manufacturing and operation and may adversely affect the electrical performance and long-term reliability of photovoltaic cells. Detecting these defects in photovoltaic defect images is challenging due to complex background textures, scale variations, and structural discontinuity of defects. To address these challenges, we propose an asymmetric cross-layer fusion network (ACFNet) for photovoltaic cell defect detection. Specifically, we design a spatial-channel modulating block (SCMB) into the backbone to suppress background interference through joint spatial and channel modulation. We propose an asymmetric cross-layer fusion (ACF) strategy to handle scale variations by performing multiscale feature aggregation via asymmetric cross-layer connections. Furthermore, we develop a semantic context enhancer (SCE) in the detector heads to enhance the representation of discontinuous defect structures by combining large-kernel convolutions with parallel attention mechanisms. Experimental results on the PVEL-AD and PV-Multi-Defect datasets show that ACFNet achieves mAP@0.5 values of 0.933 and 0.884 and mAP@0.5:0.95 values of 0.652 and 0.598, respectively, with 3.0 M parameters and a measured throughput of 75.7 FPS on an NVIDIA A100 GPU, equivalent to an average processing time of approximately 13.2 ms per image.