SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning

Self-supervised learning (SSL), utilizing unlabeled datasets for training powerful encoders, has achieved significant success recently. These encoders serve as feature extractors for downstream tasks, requiring substantial resources. However, the challenge of protecting the intellectual property of encoder trainers and ensuring the trustworthiness of deployed encoders remains a significant gap in SSL. Moreover, recent researches highlight threats to pre-trained encoders, such as backdoor and adversarial attacks. To address these gaps, we propose SSL-Auth, the first authentication framework designed specifically for pre-trained encoders. In particular, SSL-Auth utilizes selected key samples as watermark information and trains a verification network to reconstruct the watermark information, thereby verifying the integrity of the encoder without compromising model performance. By comparing the reconstruction results of the key samples, malicious alterations can be detected, as modified encoders won't mimic the original reconstruction. Comprehensive evaluations on various encoders and diverse downstream tasks demonstrate the effectiveness and fragility of our proposed SSL-Auth.Comment: Submitted to AAAI2024. 9 pages, 7 figure

A Unified Framework for Analyzing and Detecting Malicious Examples of DNN Models

Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input can mislead the models to give wrong results. Although defenses against adversarial attacks have been widely studied, research on mitigating backdoor attacks is still at an early stage. It is unknown whether there are any connections and common characteristics between the defenses against these two attacks. In this paper, we present a unified framework for detecting malicious examples and protecting the inference results of Deep Learning models. This framework is based on our observation that both adversarial examples and backdoor examples have anomalies during the inference process, highly distinguishable from benign samples. As a result, we repurpose and revise four existing adversarial defense methods for detecting backdoor examples. Extensive evaluations indicate these approaches provide reliable protection against backdoor attacks, with a higher accuracy than detecting adversarial examples. These solutions also reveal the relations of adversarial examples, backdoor examples and normal samples in model sensitivity, activation space and feature space. This can enhance our understanding about the inherent features of these two attacks, as well as the defense opportunities

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Combined economic/emission dispatch (CEED) is generally studied using analytical objective functions. However, for large-scale, high-dimension power systems, CEED problems are transformed into computationally expensive CEED (CECEED) problems, for which existing approaches are time-consuming and may not obtain satisfactory solutions. To overcome this problem, a novel data-driven surrogate-assisted method is introduced firstly. The fuel cost and emission objective functions are replaced by improved Kriging-based surrogate models. A new infilling sampling strategy for updating Kriging-based surrogate models online is proposed, which improves their fitting accuracy. Through this way, the evaluation time of the objective functions is significantly reduced. Secondly, the optimization of CECEED is executed by an improved non-dominated sorting genetic algorithm-II (NSGA-II). The above infilling sampling strategy is also used to reduce the number of evaluations for original mathematic fitness functions. To improve their local convergence ability and global search abilities, the individuals that exhibit excellent performance in a single objective are cloned and mutated. Finally, information about the Pareto front is used to guide individuals to search for better solutions. The effectiveness of this optimization method is demonstrated through simulations of IEEE 118-bus test system and IEEE 300-bus test system

Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning

Learning-based approaches have achieved remarkable performance in the domain of autonomous driving. Leveraging the impressive ability of neural networks and large amounts of human driving data, complex patterns and rules of driving behavior can be encoded as a model to benefit the autonomous driving system. Besides, an increasing number of data-driven works have been studied in the decision-making and motion planning module. However, the reliability and the stability of the neural network is still full of uncertainty. In this paper, we introduce a hierarchical planning architecture including a high-level grid-based behavior planner and a low-level trajectory planner, which is highly interpretable and controllable. As the high-level planner is responsible for finding a consistent route, the low-level planner generates a feasible trajectory. We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios.Comment: 6 pages, 8 figures, accepted by IROS202

Flight testing verification of lateral-directional dynamic stability of gliding birds due to wing dihedral

Purpose Unlike conventional aircraft, birds can glide without a vertical tail. The purpose of this paper is to analyse the influence of dihedral angle spanwise distribution on lateral-directional dynamic stability by the simulation, calculation in the development of the bird-inspired aircraft and the flight testing. Design/methodology/approach The gliding magnificent frigatebird (Fregata magnificens) was selected as the study object. The geometric and mass model of the study object were developed. Stability derivatives and moments of inertia were obtained. The lateral-directional stability was assessed under different spanwise distributions of dihedral angle. A bird-inspired aircraft was developed, and a flight test was carried out to verify the analysed results. Findings The results show that spanwise distribution changing of dihedral angle has influence on the lateral-directional mode stability. All of the analysed configurations have convergent Dutch roll mode and rolling mode. The key role of dihedral angle changing is to achieve a convergent spiral mode. Flight test results show that the bird-inspired aircraft has a well-convergent Dutch roll mode. Practical implications The theory that birds can achieve its lateral-directional stability by changing its dihedral angle spanwise distribution may explain the stability mechanism of gliding birds. Originality/value This paper helps to improve the understanding of bird gliding stability mechanism and provides bio-inspired solutions in aircraft designing

Quasi-1D graphene superlattices formed on high index surfaces

We report preparation of large area quasi-1D monolayer graphene superlattices on a prototypical high index surface Cu(410)-O and characterization by Raman spectroscopy, Auger electron spectroscopy (AES), low energy electron diffraction (LEED), scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS). The periodically stepped substrate gives a 1D modulation to graphene, forming a superlattice of the same super-periodicity. Consequently the moire pattern is also quasi-1D, with a different periodicity. Scanning tunneling spectroscopy measurements revealed new Dirac points formed at the superlattice Brillouin zone boundary as predicted by theories.Comment: 4 figure

20(S)-Protopanaxadiol Inhibits Titanium Particle-Induced Inflammatory Osteolysis and RANKL-Mediated Osteoclastogenesis via MAPK and NF-κB Signaling Pathways

Osteolysis is a principal reason for arthroplasty failure like aseptic loosening induced by Titanium (Ti) particle. It is a challenge for orthopedic surgeons. Recent researches show that 20(S)-protopanaxadiol can inhibit inflammatory cytokine release in vitro. This study aims to assess the effect of 20(S)-protopanaxadiol on Ti particle-induced osteolysis and RANKL-mediated osteoclastogenesis. Micro-CT and histological analysis in vivo indicated the inhibitory effects of 20(S)-protopanaxadiol on osteoclastogenesis and the excretion of inflammatory cytokines. Next, we demonstrated that 20(S)-protopanaxadiol inhibited osteoclast differentiation, bone resorption area, and F-actin ring formation in a dose-dependent manner. Moreover, mechanistic studies suggested that the suppression of MAPK and NF-κB signaling pathways were found to mediate the inhibitory effects of 20(S)-protopanaxadiol. In conclusion, 20(S)-protopanaxadiol may suppress osteoclastogenesis in a dose- dependent manner and it could be a potential treatment of Ti particle-induced osteolysis