Recent advances in deep learning have enabled highly accurate six-degree-of-freedom (6DoF) object pose estimation, leading to its widespread use in real-world applications such as robotics, augmented reality, virtual reality, and autonomous systems. However, backdoor attacks pose a major security risk to deep learning models. By injecting malicious triggers into training data, an attacker can cause a model to perform normally on benign inputs but behave incorrectly under specific conditions. While most research on backdoor attacks has focused on 2D vision tasks, their impact on 6DoF pose estimation remains largely unexplored. Furthermore, unlike traditional backdoors that only change the object class, backdoors against 6DoF pose estimation must additionally control continuous pose parameters, such as translation and rotation, making existing 2D backdoor attack methods not directly applicable to this setting. To address this gap, we propose a novel backdoor attack framework (6DAttack) that exposes vulnerabilities in 6DoF pose estimation. 6DAttack uses synthetic and real 3D objects of varying shapes as triggers and assigns target poses to induce controlled erroneous pose outputs while maintaining normal behavior on clean inputs. We evaluated this attack on multiple models (including PVNet, DenseFusion, and PoseDiffusion) and datasets (including LINEMOD, YCB-Video, and CO3D). Experimental results demonstrate that 6DAttack achieves extremely high attack success rates (ASRs) without compromising performance on legitimate tasks. Across various models and objects, the backdoored models achieve up to 100% ADD accuracy on clean data, while also achieving 100% ASR under trigger conditions. The accuracy of controlled erroneous pose output is also extremely high, with triggered samples achieving 97.70% ADD-P. These results demonstrate that the backdoor can be reliably implanted and activated, achieving a high ASR under trigger conditions while maintaining a negligible impact on benign data. Furthermore, we evaluate a representative defense and show that it remains ineffective under 6DAttack. Overall, our findings reveal a potentially serious and previously underexplored threat to modern 6DoF pose estimation models.

Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs). However, the misuse of AIGTs could have profound implications for public opinion, such as spreading misinformation and manipulating narratives. Despite its importance, it remains unclear how prevalent AIGTs are on social media. To address this gap, this paper aims to quantify and monitor the AIGTs on online social media platforms. We first collect a dataset (SM-D) with around 2.4M posts from 3 major social media platforms: Medium, Quora, and Reddit. Then, we construct a diverse dataset (AIGTBench) to train and evaluate AIGT detectors. AIGTBench combines popular open-source datasets and our AIGT datasets generated from social media texts by 12 LLMs, serving as a benchmark for evaluating mainstream detectors. With this setup, we identify the best-performing detector (OSM-Det). We then apply OSM-Det to SM-D to track AIGTs across social media platforms from January 2022 to October 2024, using the AI Attribution Rate (AAR) as the metric. Specifically, Medium and Quora exhibit marked increases in AAR, rising from 1.77% to 37.03% and 2.06% to 38.95%, respectively. In contrast, Reddit shows slower growth, with AAR increasing from 1.31% to 2.45% over the same period. Our further analysis indicates that AIGTs on social media differ from human-written texts across several dimensions, including linguistic patterns, topic distributions, engagement levels, and the follower distribution of authors. We envision our analysis and findings on AIGTs in social media can shed light on future research in this domain.

The rising popularity of large language models (LLMs) has raised concerns about potential abuse and harmful content. As a result, developing a highly generalizable and adaptable machine-generated text (MGT) detection system has become an urgent priority. Given that LLMs are most commonly misused in academic writing, this work investigates the generalization and adaptation capabilities of MGT detectors in three key aspects specific to academic writing: First, we construct MGT-Academic, a large-scale dataset comprising over 336M tokens and 749K samples. MGT-Academic focuses on academic writing, featuring human-written texts (HWTs) and MGTs across STEM, Humanities, and Social Sciences, paired with an extensible code framework for efficient benchmarking. Second, we benchmark the performance of various detectors for binary classification and text attribution tasks in both in-domain and cross-domain settings. This benchmark reveals the often-overlooked challenges of text attribution tasks. Third, we introduce a novel text attribution task in which models must adapt to new classes over time, with little or no access to prior training data, spanning both few-shot and many-shot scenarios. We implement a range of adaptation techniques to enhance performance across these settings. Our findings provide new insights into the generalization ability of MGT detectors and lay the foundation for building robust, adaptive detection systems. The code framework is available at https://github.com/Y-L-LIU/MGTBench-2.0.

As Large Language Models (LLMs) advance, Machine-Generated Texts (MGTs) have become increasingly fluent, high-quality, and informative. Existing wide-range MGT detectors are designed to identify MGTs to prevent the spread of plagiarism and misinformation. However, adversaries attempt to humanize MGTs to evade detection (named evading attacks), which requires only minor modifications to bypass MGT detectors. Unfortunately, existing attacks generally lack a unified and comprehensive evaluation framework, as they are assessed using different experimental settings, model architectures, and datasets. To fill this gap, we introduce the Text-Humanization Benchmark (TH-Bench), the first comprehensive benchmark to evaluate evading attacks against MGT detectors. TH-Bench evaluate attacks across three key dimensions: evading effectiveness, text quality, and computational overhead. Our extensive experiments evaluate 6 state-of-the-art attacks against 13 MGT detectors across 6 datasets, spanning 19 domains and generated by 11 widely used LLMs. Our findings reveal that no single evading attack excels across all three dimensions. Through in-depth analysis, we highlight the strengths and limitations of different attacks. More importantly, we identify a trade-off among three dimensions and propose two optimization insights. Through preliminary experiments, we validate their correctness and effectiveness, offering potential directions for future research.

Current benchmarks for evaluating large language models (LLMs) in social media moderation completely overlook a serious threat: covert advertisements, which disguise themselves as regular posts to deceive and mislead consumers into making purchases, leading to significant ethical and legal concerns. In this paper, we present the CHASM, a first-of-its-kind dataset designed to evaluate the capability of Multimodal Large Language Models (MLLMs) in detecting covert advertisements on social media. CHASM is a high-quality, anonymized, manually curated dataset consisting of 4,992 instances, based on real-world scenarios from the Chinese social media platform Rednote. The dataset was collected and annotated under strict privacy protection and quality control protocols. It includes many product experience sharing posts that closely resemble covert advertisements, making the dataset particularly challenging.The results show that under both zero-shot and in-context learning settings, none of the current MLLMs are sufficiently reliable for detecting covert advertisements.Our further experiments revealed that fine-tuning open-source MLLMs on our dataset yielded noticeable performance gains. However, significant challenges persist, such as detecting subtle cues in comments and differences in visual and textual structures.We provide in-depth error analysis and outline future research directions. We hope our study can serve as a call for the research community and platform moderators to develop more precise defenses against this emerging threat.

Fine-tuning is an essential process to improve the performance of Large Language Models (LLMs) in specific domains, with Parameter-Efficient Fine-Tuning (PEFT) gaining popularity due to its capacity to reduce computational demands through the integration of low-rank adapters. These lightweight adapters, such as LoRA, can be shared and utilized on open-source platforms. However, adversaries could exploit this mechanism to inject backdoors into these adapters, resulting in malicious behaviors like incorrect or harmful outputs, which pose serious security risks to the community. Unfortunately, few current efforts concentrate on analyzing the backdoor patterns or detecting the backdoors in the adapters. To fill this gap, we first construct and release PADBench, a comprehensive benchmark that contains 13, 300 benign and backdoored adapters fine-tuned with various datasets, attack strategies, PEFT methods, and LLMs. Moreover, we propose PEFTGuard, the first backdoor detection framework against PEFT-based adapters. Extensive evaluation upon PADBench shows that PEFTGuard outperforms existing detection methods, achieving nearly perfect detection accuracy (100%) in most cases. Notably, PEFTGuard exhibits zero-shot transferability on three aspects, including different attacks, PEFT methods, and adapter ranks. In addition, we consider various adaptive attacks to demonstrate the high robustness of PEFTGuard. We further explore several possible backdoor mitigation defenses, finding fine-mixing to be the most effective method. We envision that our benchmark and method can shed light on future LLM backdoor detection research.11Our code and dataset are available at: https://github.com/Vincent-HKUSTGZ/PEFTGuard.

AI-assisted operating room scene understanding is essential for the next generation of surgical interventions. Surgical instrument localization plays an important role in this context. However, existing instrument localization methods primarily focus on surgical instrument localization in endoscopy images and struggle with occlusions in broader operating room scenarios. In this work, we propose a weakly supervised instance segmentation framework, Pixel-driven Weakly-supervised Instance Segmentation (PWISeg), to solve the occluded instrument localization with low-cost annotations. Specifically, We utilize the projection relationship between the bounding box and the surgical instrument mask as a supervision signal to train PWISeg to predict coarse masks of surgical instruments. Then, we use the annotation of a few pixel points to train PWISeg to predict accurate masks of surgical instruments. To extensively validate the effectiveness, we collect and release a high-quality dataset, Surg-Inst that covers real-world hard cases of overlapping, dense placement, and various levels of instrument occlusion. Experiments demonstrate that our PWISeg achieves a remarkable performance advantage over state-of-the-art methods on both Surg-Inst and public HOSPI-Tools datasets.