Generative large language models (LLMs) have revolutionized natural language processing with their transformative and emergent capabilities. However, recent evidence indicates that LLMs can produce harmful content that violates social norms, raising significant concerns regarding the safety and ethical ramifications of deploying these advanced models. Thus, it is both critical and imperative to perform a rigorous and comprehensive safety evaluation of LLMs before deployment. Despite this need, owing to the extensiveness of LLM generation space, it still lacks a unified and standardized risk taxonomy to systematically reflect the LLM content safety, as well as automated safety assessment techniques to explore the potential risks efficiently. To bridge the striking gap, we propose S-Eval, a novel LLM-based automated Safety Evaluation framework with a newly defined comprehensive risk taxonomy. S-Eval incorporates two key components, i.e., an expert testing LLM M _t and a novel safety critique LLM M _c . The expert testing LLM M _t is responsible for automatically generating test cases in accordance with the proposed risk management (including 8 risk dimensions and a total of 102 subdivided risks). The safety critique LLM M _c can provide quantitative and explainable safety evaluations for better risk awareness of LLMs. In contrast to prior works, S-Eval differs in significant ways: (i) efficient – we construct a multi-dimensional and open-ended benchmark comprising 220,000 test cases across 102 risks utilizing M _t and conduct safety evaluations for 21 influential LLMs via M _c on our benchmark. The entire process is fully automated and requires no human involvement. (ii) effective – extensive validations show S-Eval facilitates a more thorough assessment and better perception of potential LLM risks, and M _c not only accurately quantifies the risks of LLMs but also provides explainable and in-depth insights into their safety, surpassing comparable models such as LLaMA-Guard-2. (iii) adaptive – S-Eval can be flexibly configured and adapted to the rapid evolution of LLMs and accompanying new safety threats, test generation methods and safety critique methods thanks to the LLM-based architecture. We further study the impact of hyper-parameters and language environments on model safety, which may lead to promising directions for future research. S-Eval has been deployed in our industrial partner for the automated safety evaluation of multiple LLMs serving millions of users, demonstrating its effectiveness in real-world scenarios.

Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice. Recent advances in generative adversarial models have rendered them particularly effective in MI attacks, primarily due to their ability to generate high-fidelity and perceptually realistic images that closely resemble the target data. In this work, we propose a novel Dynamic Memory Model Inversion Attack (DMMIA) to leverage historically learned knowledge, which interacts with samples (during the training) to induce diverse generations. DMMIA constructs two types of prototypes to inject the information about historically learned knowledge: Intra-class Multicentric Representation (IMR) representing target-related concepts by multiple learnable prototypes, and Inter-class Discriminative Representation (IDR) characterizing the memorized samples as learned prototypes to capture more privacy-related information. As a result, our DMMIA has a more informative representation, which brings more diverse and discriminative generated results. Experiments on multiple benchmarks show that DMMIA performs better than state-of-the-art MI attack methods.

Contrastive Language-Image Pre-trained (CLIP) models have zero-shot ability of classifying an image belonging to "[CLASS]" by using similarity between the image and the prompt sentence "a [CONTEXT] of [CLASS]". Based on exhaustive text cues in "[CONTEXT]", CLIP model is aware of different contexts, e.g. background, style, viewpoint, and exhibits unprecedented robustness against a wide range of distribution shifts. However, recent works find further fine-tuning of CLIP models improves accuracy but sacrifices the robustness on downstream tasks. We conduct an empirical investigation to show fine-tuning will corrupt the context-aware ability of pre-trained CLIP features. To solve this problem, we propose Context-Aware Robust Fine-tuning (CAR-FT). CAR-FT regularizes the model during fine-tuning to capture the context information. Specifically, we use zero-shot prompt weights to get the context distribution contained in the image. By minimizing the Kullback-Leibler Divergence (KLD) between context distributions induced by original/fine-tuned CLIP models, CAR-FT makes the context-aware ability of CLIP inherited into downstream tasks, and achieves both higher In-Distribution (ID) and Out-Of-Distribution (OOD) accuracy. The experimental results show CAR-FT achieves superior robustness on five OOD test datasets of ImageNet, and meanwhile brings accuracy gains on nine downstream tasks. Additionally, CAR-FT surpasses previous Domain Generalization (DG) methods and gets 78.5% averaged accuracy on DomainBed benchmark, building the new state-of-the-art.

Thermal infrared pedestrian tracking is a challenging task due to factors such as energy attenuation, sensor noise, occlusion, and complex backgrounds. In this paper, we design a two-level cascade model that tracks pedestrians in a thermal infrared video by the coarse-to-fine strategy to improve the tracking accuracy and success rate. The base tracker in the first level of our model is initialized and fine-tuned to get the first representation of a target which is then used to locate the target roughly. Aiming at finely locating a target, the second level consists of modality-specific part correlation filters that can capture patterns of thermal infrared pedestrians. The outputs of part correlation filters are aggregated together by normalized joint confidence that can effectively suppress low confidence predictions to make a final decision. We adaptively update each part filter by a weighted learning rate and accurately estimate pedestrian scale by a scale filter to improve tracking performance. The experimental results on the PTB-TIR benchmark show that the proposed cascade tracker further emphasizes crucial thermal infrared features. Thus it can effectively relieve the problem of object occlusion. Our experimental results show the superiority of the proposed tracker over the state-of-the-art trackers, including SRDCF, GFS-DCF, MCFTS, HDT, HCF, MLSSNet, HSSNet, SiamFC_tir, SVM, and L1APG.