Face recognition (FR) brings convenience to peoples lives while also posing security risks. Some malicious users employ FR attacks to impersonate the identity of a target. To reveal the security risks, recent work has attacked black-box FR models by utilizing substitute models to generate adversarial face images that are misclassified as the target individual due to the attack transferability of substitute models. However, the substitute models cannot accurately approximate the target model that leads to a decrease in FR attack success rate and adversarial face image quality. To address the issue, we propose the PPOM-Attack, a substitute model-free Perturbation Prediction and Optimization Method for black-box adversarial Attack against face recognition. PPOM-Attack directly obtains feedback from the target model instead of using substitute models, it avoids any discrepancy with the attack objective. To achieve this goal, we design a proximal policy optimization (PPO)-based agent to predict the perturbation regions in the face image and self-adaptively disturb the regions. To maintain high-quality adversarial face images, we further propose a minimum brightness offsets method specifically designed to generate perturbations that minimize the feature embedding difference between the adversarial and targeted face images. The experimental results show that our approach outperforms state-of-the-art FR attack methods by an average of 21.7% in terms of attack success rate, while achieving better image quality on seven FR models.

The automation of very large-scale PCB routing has long been an unresolved problem within the industry due to the variant electronic components and complex design rules. Existing automated PCB routing methods are primarily designed for single component (e.g., BGA, BTB, etc.) or for simple and small-scale PCBs, and often fail to meet the industry requirements for large-scale PCBs. The biggest challenge is to ensure nearly 100% routability and DRC compliance while achieving high efficiency for large-scale PCBs with various components. To address this challenge, we propose MegaRoute, a precise, efficient, and universal PCB routing method that surpasses the routing routability and DRC compliance of existing methods, including commercial tools, for PCBs with thousands of nets. MegaRoute introduces an adaptive step-size search algorithm that adjusts exploration steps based on design rules and surrounding obstacles, improving both routability and efficiency. We incorporate shape-based obstacle detection for strict DRC compliance and use routing optimization techniques to enhance routability. We conduct extensive experiments on hundreds of real-world PCBs, including mainboard PCBs with thousands of nets. The results show that MegaRoute achieves over 98% routability across all PCBs with DRC-free results, significantly outperforming the state-of-the-art methods and mainstream commercial tools.

Voice Conversion (VC) technologies have advanced significantly, enabling voice cloning with just a few seconds of audio, posing serious risks to privacy, property, and reputation. In response to these threats, adversarial defense methods protect users by adding imperceptible perturbations to the audio, making it harder for VC models to clone the original voice. However, current methods are effective in white-box scenarios but struggle in black-box settings where VC models' internal parameters and structures are unknown. To address this problem, we propose a black-box adversarial defense method that effectively adapts and defends against unknown VC models relies solely on black-box feedback. We introduce a latent perturbation model that compresses speech and generates initial perturbations to reduce the search space and ensure convergence for black-box optimization. We then apply evolution-based black-box optimization to refine the perturbations, improving defense performance against unknown VC models. Extensive experiments on state-of-the-art VC models demonstrate the adaptability and superior defense performance of our method in real-world black-box scenarios compared to other defence approaches. Demo is available at https://g-bety.github.io/advdense.

Abstract The rapid advancement of large language models (LLMs) has opened up promising opportunities for their downstream applications in question-answering (QA), such as ChatGPT, ChatGLM, etc. However, such LLMs do not perform very well in domain-specific QA tasks without fine-tuning. But directly fine-tuning LLMs on domain-specific corpus data may lead to catastrophic forgetting, causing the LLMs to lose their general language capability. To address this problem, we propose the Knowledge-Enhanced Fine-Tuning (KEFT) method, an unsupervised fine-tuning approach to enhance the knowledge capability of LLMs in domain-specific QA tasks while preserving their general language capability. KEFT leverages the inherent language comprehension of pre-trained LLMs to generate synthetic-QA datasets from domain-specific corpus data autonomously for fine-tuning, and adopts a Low-Rank Adaptation (LoRA) method to further alleviate over-fitting. Furthermore, to enhance the representation of domain-specific knowledge, we introduce a knowledge-enhanced fine-tuning loss function, which encourages the model to learn the knowledge-question connection, thereby generating natural and knowledgeable answers. Our evaluations across multiple domain-specific datasets demonstrate that KEFT surpasses state-of-the-art fine-tuning approaches, enhancing the performance of various LLMs in QA tasks in both English and Chinese languages.

In modern electronic manufacturing processes, multi-layer Printed Circuit Board (PCB) routing requires connecting more than hundreds of nets with perplexing topology under complex routing constraints and highly limited resources, so that takes intense effort and time of human engineers. PCB fanout as a pre-design of PCB routing has been proved to be an ideal technique to reduce the complexity of PCB routing by pre-allocating resources and pre-routing. However, current PCB fanout design heavily relies on the experience of human engineers, and there is no existing solution for PCB fanout automation in industry, which limits the quality of PCB routing automation. To address the problem, we propose a neuralized PCB fanout method by deep reinforcement learning. To the best of our knowledge, we are the first in the literature to propose the automation method for PCB fanout. We combine with Convolution Neural Network (CNN) and attention-based network to train our fanout policy model and value model. The models learn representations of PCB layout and netlist to make decisions and evaluations in place of human engineers. We employ Proximal Policy Optimization (PPO) to update the parameters of the models. In addition, we apply our PCB fanout method to a PCB router to improve the quality of PCB routing. Extensive experimental results on real-world industrial PCB benchmarks demonstrate that our approach achieves 100% routability in all industrial cases and improves wire length by an average of 6.8%, which makes a significant improvement compared with the state-of-the-art methods.

Clustering is an important technology in data mining, which attempts to partition a set of objects into clusters based on the values of their attributes. [Formula: see text]-means is a simple and efficient data clustering algorithm. However, it highly depends on the initial solution and is extremely easy to be trapped in local optima. In contrast, meta-heuristic algorithms show good performance to break through the local optima obstacle. In this paper, we propose an improved pigeon-inspired optimization (IPIO) algorithm towards resolving this problem. The algorithm uses an object-based initialization method to generate the initial population and introduces a parametric control strategy to navigate the flying direction. Meanwhile, the climb process of monkey algorithm (MA) with dimension by dimension improvement is adopted to strengthen the local search ability. In this paper, experiments over six real datasets are conducted to validate the effectiveness of IPIO. The experimental results show that IPIO is an efficient alternative in resolving the clustering analysis problem.