19 accepted papers
Text-to-image (T2I) models have raised increasing safety concerns due to their capacity to generate NSFW and other banned objects. To mitigate these risks, safety filters and concept removal techniques have been introduced to block inappropriate prompts or erase sensitive concepts from the models. H
In recent years, transformer-based models have achieved remarkable success in sensitive domains, including healthcare, finance and personalized services, but their deployment raises significant privacy concerns. Existing secure inference studies have introduced cryptographic techniques such as Homom
Text-to-Image diffusion models have achieved remarkable success in image generation and are increasingly fine-tuned for personalized use cases. However, many personalized models may incorporate unauthorized data (e.g., copyrighted materials) during the fine-tuning process, raising growing concerns a…
This paper presents a hierarchical control framework for quadrupedal locomotion that unifies the complementary strengths of model-based optimization and reinforcement learning. We develop a convex Quadratic Programming~(QP) solver based on the primal-dual Chambolle-Pock algorithm, enabling both mass…
Large language models (LLMs) are inherently designed to support multi-turn interactions, which opens up new possibilities for jailbreak attacks that unfold gradually and potentially bypass safety mechanisms more effectively than single-turn attacks. However, current multi-turn jailbreak methods are…
Text-to-image models have achieved remarkable progress in generating high-quality images from textual prompts, yet their potential for misuse like generating unsafe content remains a critical concern. Existing safety mechanisms, such as filtering and fine-tuning, remain insufficient in preventing vu…
With the rapid development of the internet, sharing photos through Social Network Platforms (SNPs) has become a new way for people to socialize, which poses serious threats to personal privacy. Recently, a thumbnail-preserving image privacy protection technique has emerged and garnered widespread at
In recent years, Text-to-Image (T2I) models have garnered significant attention due to their remarkable advancements. However, security concerns have emerged due to their potential to generate inappropriate or Not-Safe-For-Work (NSFW) images. In this paper, inspired by the observation that texts wit…
Text-to-image (T2I) diffusion models have achieved impressive image generation quality and are increasingly fine-tuned for personalized applications. However, these models often inherit unsafe behaviors from toxic pretraining data, raising growing safety concerns. While recent safety-driven unlearni…
In the last few years, the artifact patterns in fake images synthesized by different generative models have been inconsistent, leading to the failure of previous research that relied on spotting subtle differences between real and fake. In our preliminary experiments, we find that the artifacts in f…
RAW to sRGB mapping, which aims to convert RAW images from smartphones into RGB form equivalent to that of Digital Single-Lens Reflex (DSLR) cameras, has become an important area of research. However, current methods often ignore the difference between cell phone RAW images and DSLR camera RGB image…
In recent years, DeepFake technology has achieved unprecedented success in high-quality video synthesis, but these methods also pose potential and severe security threats to humanity. DeepFake can be bifurcated into entertainment applications like face swapping and illicit uses such as lip-syncing f…
As an important research direction in the field of speech signal processing, cross-domain speech emotion recognition (SER) has attracted extensive attention. In practice, it is challenging to collect enough labeled samples from single source domain to train robust classifiers. To this end, this pape…
Model quantization is a compression technique that converts a full-precision model to a more compact low-precision version for better storage. Despite the great success of quantization, recent studies revealed the feasibility of malicious exploiting model quantization via implanting quantization-con…
In recent few years, DeepFakes are posing serve threats and concerns to both individuals and celebrities, as realistic DeepFakes facilitate the spread of disinformation. Model attribution techniques aim at attributing the adopted forgery models of DeepFakes for provenance purposes and providing expl…
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis. However, well-trained GANs are under the threat of illegal steal or leakage. The prior studies on remote ownership verification assume a black-box setting where the defend…
DeepFake is becoming a real risk to society and brings potential threats to both individual privacy and political security due to the DeepFaked multimedia are realistic and convincing. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and failed in blocking the d…
As deep neural networks (DNNs) play a critical role in various fields, the models themselves hence are becoming an important asset that needs to be protected. To achieve this, various neural network fingerprint methods have been proposed. However, existing fingerprint methods fingerprint the decisio…
In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation. However, robust d…