Search

Transferable adversarial examples cause practical security risks since they can mislead a target model without knowing its internal knowledge. A conventional recipe for maximizing transferability is to keep only the optimal adversarial example from all those obtained in the optimization pipeline. In this paper, for the first time, we question this convention and demonstrate that those discarded, sub-optimal adversarial examples can be reused to boost transferability. Specifically, we propose ``Adversarial Example Soups'' (AES), with AES-tune for averaging discarded adversarial examples in hyperparameter tuning and AES-rand for stability testing. In addition, our AES is inspired by ``model soups'', which averages weights of multiple fine-tuned models for improved accuracy without increasing inference time. Extensive experiments validate the global effectiveness of our AES, boosting 10 state-of-the-art transfer attacks and their combinations by up to 13% against 10 diverse (defensive) target models. We also show the possibility of generalizing AES to other types, e.g., directly averaging multiple in-the-wild adversarial examples that yield comparable success. A promising byproduct of AES is the improved stealthiness of adversarial examples since the perturbation variances are naturally reduced., Comment: Under review

Quantized neural networks (QNNs) have received increasing attention in resource-constrained scenarios due to their exceptional generalizability. However, their robustness against realistic black-box adversarial attacks has not been extensively studied. In this scenario, adversarial transferability is pursued across QNNs with different quantization bitwidths, which particularly involve unknown architectures and defense methods. Previous studies claim that transferability is difficult to achieve across QNNs with different bitwidths on the condition that they share the same architecture. However, we discover that under different architectures, transferability can be largely improved by using a QNN quantized with an extremely low bitwidth as the substitute model. We further improve the attack transferability by proposing \textit{quantization aware attack} (QAA), which fine-tunes a QNN substitute model with a multiple-bitwidth training objective. In particular, we demonstrate that QAA addresses the two issues that are commonly known to hinder transferability: 1) quantization shifts and 2) gradient misalignments. Extensive experimental results validate the high transferability of the QAA to diverse target models. For instance, when adopting the ResNet-34 substitute model on ImageNet, QAA outperforms the current best attack in attacking standardly trained DNNs, adversarially trained DNNs, and QNNs with varied bitwidths by 4.3\% $\sim$ 20.9\%, 8.7\% $\sim$ 15.5\%, and 2.6\% $\sim$ 31.1\% (absolute), respectively. In addition, QAA is efficient since it only takes one epoch for fine-tuning. In the end, we empirically explain the effectiveness of QAA from the view of the loss landscape. Our code is available at https://github.com/yyl-github-1896/QAA, Comment: Accepted by IEEE Transactions on Information Forensics and Security in 2024

Transfer-based adversarial attacks raise a severe threat to real-world deep learning systems since they do not require access to target models. Adversarial training (AT), which is recognized as the strongest defense against white-box attacks, has also guaranteed high robustness to (black-box) transfer-based attacks. However, AT suffers from heavy computational overhead since it optimizes the adversarial examples during the whole training process. In this paper, we demonstrate that such heavy optimization is not necessary for AT against transfer-based attacks. Instead, a one-shot adversarial augmentation prior to training is sufficient, and we name this new defense paradigm Data-centric Robust Learning (DRL). Our experimental results show that DRL outperforms widely-used AT techniques (e.g., PGD-AT, TRADES, EAT, and FAT) in terms of black-box robustness and even surpasses the top-1 defense on RobustBench when combined with diverse data augmentations and loss regularizations. We also identify other benefits of DRL, for instance, the model generalization capability and robust fairness., Comment: 9 pages

Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE). Nevertheless, recent studies have revealed that adversarially trained models are prone to unfairness problems, restricting their applicability. In this paper, we empirically observe that this limitation may be attributed to serious adversarial confidence overfitting, i.e., certain adversarial examples with overconfidence. To alleviate this problem, we propose HAM, a straightforward yet effective framework via adaptive Hard Adversarial example Mining.HAM concentrates on mining hard adversarial examples while discarding the easy ones in an adaptive fashion. Specifically, HAM identifies hard AEs in terms of their step sizes needed to cross the decision boundary when calculating loss value. Besides, an early-dropping mechanism is incorporated to discard the easy examples at the initial stages of AE generation, resulting in efficient AT. Extensive experimental results on CIFAR-10, SVHN, and Imagenette demonstrate that HAM achieves significant improvement in robust fairness while reducing computational cost compared to several state-of-the-art adversarial training methods. The code will be made publicly available.

Plate-shaped nanoparticles exhibit huge potential for a broad range of cutting-edge applications in interfacial-based science and technology, such as enhanced oil recovery in hydrocarbon reservoirs, owing to their remarkable features in superior affinity toward interfaces. Understanding the adsorption behavior of nanosheets (NSs) self-assembled at the water/oil interface (W/O interface) is crucial to elucidate the variation of interfacial tension (IFT) and establish special design criteria for efficient industrial use of NSs. Here we present a molecular dynamics study to reveal the morphology of carbon-chain modified molybdenum disulfide (MoS2) nanosheets. The stress exerted on a nanosheet is analyzed. The simulation results demonstrate a significant decrease in interfacial tension after adding NSs to the water/oil system, followed by a noticeable fluctuation with increased NS concentration. Surprisingly, the carbon-chain modified MoS2 nanosheets do not show a greater ability in altering IFT compared to unmodified ones. The IFT fluctuation is found strongly linked to the competition between the effects of interface coverage rate of adsorbed nanosheets and the intersection angle arising from non-uniform forces acting on a nanosheet.

Permeability reduction and formation damage in porous media caused by fines (defined as unconfined solid particles present in the pore spaces) migration is one of the major reasons for productivity decline. It is well accepted that particle detachment occurs under imbalanced torques arising from hydrodynamic and adhesive forces exerted on attached particles. This paper reviewed current understanding on primary factors influencing fines migration as well as mathematical formulations for quantification. We also introduced salinity-related experimental observations that contradict theoretical predictions based on torque balance criteria, such as delayed particle release and attachment-detachment hysteresis. The delay of particle release during low-salinity water injection was successfully explained and formulated by the Nernst-Planck diffusion of ions in a narrow contact area. In addition to the widely recognized explanation by surface heterogeneity and the presence of low-velocity regions, we proposed a hypothesis that accounts for the shifting of equilibrium positions, providing new insight into the interpretation of elusive attachment-detachment hysteresis both physically and mathematically. The review was finalized by discussing the quantification of anomalous salinity effect on adhesion force at low- and high-salinity conditions.

Plate-shaped nanoparticles exhibit huge potential for a broad range of cutting-edge applications in interfacial-based science and technology, such as enhanced oil recovery in hydrocarbon reservoirs, owing to their remarkable features in superior affinity toward interfaces. Understanding the adsorption behavior of nanosheets (NSs) self-assembled at the water/oil interface (W/O interface) is crucial to elucidate the variation of interfacial tension (IFT) and establish special design criteria for efficient industrial use of NSs. Here we present a molecular dynamics study to reveal the morphology of carbon-chain modified molybdenum disulfide (MoS2) nanosheets. The stress exerted on a nanosheet is analyzed. The simulation results demonstrate a significant decrease in interfacial tension after adding NSs to the water/oil system, followed by a noticeable fluctuation with increased NS concentration. Surprisingly, the carbon-chain modified MoS2 nanosheets do not show a greater ability in altering IFT compared to unmodified ones. The IFT fluctuation is found strongly linked to the competition between the effects of interface coverage rate of adsorbed nanosheets and the intersection angle arising from non-uniform forces acting on a nanosheet.

Permeability reduction and formation damage in porous media caused by fines (defined as unconfined solid particles present in the pore spaces) migration is one of the major reasons for productivity decline. It is well accepted that particle detachment occurs under imbalanced torques arising from hydrodynamic and adhesive forces exerted on attached particles. This paper reviewed current understanding on primary factors influencing fines migration as well as mathematical formulations for quantification. We also introduced salinity-related experimental observations that contradict theoretical predictions based on torque balance criteria, such as delayed particle release and attachment-detachment hysteresis. The delay of particle release during low-salinity water injection was successfully explained and formulated by the Nernst-Planck diffusion of ions in a narrow contact area. In addition to the widely recognized explanation by surface heterogeneity and the presence of low-velocity regions, we proposed a hypothesis that accounts for the shifting of equilibrium positions, providing new insight into the interpretation of elusive attachment-detachment hysteresis both physically and mathematically. The review was finalized by discussing the quantification of anomalous salinity effect on adhesion force at low- and high-salinity conditions.

Plate-shaped nanoparticles exhibit huge potential for a broad range of cutting-edge applications in interfacial-based science and technology, such as enhanced oil recovery in hydrocarbon reservoirs, owing to their remarkable features in superior affinity toward interfaces. Understanding the adsorption behavior of nanosheets (NSs) self-assembled at the water/oil interface (W/O interface) is crucial to elucidate the variation of interfacial tension (IFT) and establish special design criteria for efficient industrial use of NSs. Here we present a molecular dynamics study to reveal the morphology of carbon-chain modified molybdenum disulfide (MoS2) nanosheets. The stress exerted on a nanosheet is analyzed. The simulation results demonstrate a significant decrease in interfacial tension after adding NSs to the water/oil system, followed by a noticeable fluctuation with increased NS concentration. Surprisingly, the carbon-chain modified MoS2 nanosheets do not show a greater ability in altering IFT compared to unmodified ones. The IFT fluctuation is found strongly linked to the competition between the effects of interface coverage rate of adsorbed nanosheets and the intersection angle arising from non-uniform forces acting on a nanosheet.

Free amino acids (FAAs) are key components of the global nitrogen cycle and important disinfection byproduct (DBP) precursors. The knowledge gap of FAA occurrence in source and engineered water is discussed in this paper. Solid phase extraction and post column derivatization was combined with gas chromatography–mass spectrometry to simultaneously detect μg/L concentrations of FAAs. This method efficiently detects alanine (Ala), threonine (Thr), serine (Ser), valine (Val), leucine (Leu), isoleucine (Ile), proline (Pro), aspartic (Asp), phenylalanine (Phe), and glutamic acid (Glu) with good linearity, accuracy, and precision. An investigation of FAAs in surface waters in Zhejiang Province found concentrations of 1.48–14.73 μg/L Ala, 0.20–2.39 μg/L Thr, 0.41–7.84 μg/L Val, 0.21–6.86 μg/L Ser, 0.11–4.16 μ;g/L Leu, 0.57–1.54 μg/L Ile, 0.24–8.06 μg/L Pro, 0.42–4.73 μg/L Asp, 0.30–3.01 μg/L Phe, and 0.12–3.83 μg/L Glu. Phe and tyrosine (Tyr) exhibited higher trichloromethane (TCM) formation (1029–1148 μg/mmolAA) than dichloroacetonitrile (DCAN) formation (333–347 μg/mmolAA). Asp and Glu demonstrated the opposite trend: higher DCAN (570–1106 μg/mmolAA) formation than TCM (137–506 μg/mmolAA).

Gas drilling technology has been widely promoted and applied in recent years. Known for being capable of discovering and protecting reservoirs, improving the penetration rate and avoiding loss circulation, two key issues of gas drilling still need to be addressed. First, a more accurate way of determining the gas injection rate is needful. In this text, we present a modified mathematical model for predicting the optimum range of gas injection rate required to balance the borehole cleaning and well-integrity issues. The optimum gas injection rate should be sought between the minimum value required for hole cleaning and the maximum permissible value to avoid hole erosion. Good consistency between the model prediction and field problem-free nitrogen gas injection rate indicates the reliability of the proposed model. Second, the problem of environmental pollution and wasting of resources caused by direct discharging or combustion of the returned gas is to be solved. To address the latter issue, we introduce a new technology of gas recycling system (GRS). Our research group has carried out a comprehensive investigation, including integration design, technological process, cuttings transport analysis, separation and filter equipment selection, and control system design. The feasibility of GRS has been verified through an open-loop pilot test.

Gas drilling technology has been widely promoted and applied in recent years. Known for being capable of discovering and protecting reservoirs, improving the penetration rate and avoiding loss circulation, two key issues of gas drilling still need to be addressed. First, a more accurate way of determining the gas injection rate is needful. In this text, we present a modified mathematical model for predicting the optimum range of gas injection rate required to balance the borehole cleaning and well-integrity issues. The optimum gas injection rate should be sought between the minimum value required for hole cleaning and the maximum permissible value to avoid hole erosion. Good consistency between the model prediction and field problem-free nitrogen gas injection rate indicates the reliability of the proposed model. Second, the problem of environmental pollution and wasting of resources caused by direct discharging or combustion of the returned gas is to be solved. To address the latter issue, we introduce a new technology of gas recycling system (GRS). Our research group has carried out a comprehensive investigation, including integration design, technological process, cuttings transport analysis, separation and filter equipment selection, and control system design. The feasibility of GRS has been verified through an open-loop pilot test.

A considerable number of studies for vortex induced vibration (VIV) under uniform flow have been performed. However, investigation of VIV under middle flow is scarce. An experiment for VIV under middle flow was conducted in a deep-water offshore basin. Various measurements were obtained by the fiber Bragg grating strain sensors placed on the riser, and VIV under the effect of middle flow with was investigated. Results show that the riser vibrates at different order natural frequencies along the water depth in the CF and IL directions appearing as the multi-frequencies under middle flow. The variation vortex shedding frequencies along the riser under middle flow may generate different wake modes and vibration modals as the corresponding vortex shedding frequencies approach the riser natural frequencies. The dominant vibration frequency of the entire riser is consistent, and determined by high order natural frequency and the corresponding closing vortex shedding frequencies under the middle flow. Meanwhile, the vibration modal under middle flow appears multi-modals and other lower modal have effect on riser vibration. The VIV mechanism under middle flow possesses some aspects similar to those of uniform flow and several unique features.

Advances in technology have provided ways to monitor and measure driving behavior. Recently, this technology has been applied to usage-based automotive insurance policies that offer reduced insurance premiums to policy holders who opt-in to automotive monitoring. Several companies claim to measure only speed data, which they further claim preserves privacy. However, we have developed an algorithm - elastic pathing - that successfully tracks drivers' locations from speed data. The algorithm tracks drivers by assuming a start position, such as the driver's home address (which is typically known to insurance companies), and then estimates the possible routes by fitting the speed data to map data. To demonstrate the algorithm's real-world applicability, we evaluated its performance with driving datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. We are able to estimate destinations with error within 250 meters for 17% of the traces and within 500 meters for 24% of the traces in the New Jersey dataset, and with error within 250 and 500 meters for 15.5% and 27.5% of the traces, respectively, in the Seattle dataset. Our work shows that these insurance schemes enable a substantial breach of privacy., Comment: This article supersedes both arXiv:1401.0052 and the earlier work published in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'14). Please see http://elasticpathing.org for further information

This paper studies the security and memorability of free-form multitouch gestures for mobile authentication. Towards this end, we collected a dataset with a generate-test-retest paradigm where participants (N=63) generated free-form gestures, repeated them, and were later retested for memory. Half of the participants decided to generate one-finger gestures, and the other half generated multi-finger gestures. Although there has been recent work on template-based gestures, there are yet no metrics to analyze security of either template or free-form gestures. For example, entropy-based metrics used for text-based passwords are not suitable for capturing the security and memorability of free-form gestures. Hence, we modify a recently proposed metric for analyzing information capacity of continuous full-body movements for this purpose. Our metric computed estimated mutual information in repeated sets of gestures. Surprisingly, one-finger gestures had higher average mutual information. Gestures with many hard angles and turns had the highest mutual information. The best-remembered gestures included signatures and simple angular shapes. We also implemented a multitouch recognizer to evaluate the practicality of free-form gestures in a real authentication system and how they perform against shoulder surfing attacks. We conclude the paper with strategies for generating secure and memorable free-form gestures, which present a robust method for mobile authentication.

Text-based passwords continue to be the prime form of authentication to computer systems. Today, they are increasingly created and used with mobile text entry methods, such as touchscreens and mobile keyboards, in addition to traditional physical keyboards. This raises a foundational question for usable security: whether text entry methods affect password generation and password security. This paper presents results from a between-group study with 63 participants, in which each group generated passwords for multiple virtual accounts using a different text entry method. Participants were also asked to recall their passwords afterwards. We applied analysis of structures and probabilities, with standard and recent security metrics and also performed cracking attacks on the collected data. The results show a significant effect of text entry methods on passwords. In particular, one of the experimental groups created passwords with significantly more lowercase letters per password than the control group ($t(60) = 2.99, p = 0.004$). The choices for character types in each group were also significantly different ($p=0.048, FET$). Our cracking attacks consequently expose significantly different resistance across groups ($p=0.031, FET$) and text entry method vulnerabilities. Our findings contribute to the understanding of password security in the context of usable interfaces.

Today people increasingly have the opportunity to opt-in to "usage-based" automotive insurance programs for reducing insurance premiums. In these programs, participants install devices in their vehicles that monitor their driving behavior, which raises some privacy concerns. Some devices collect fine-grained speed data to monitor driving habits. Companies that use these devices claim that their approach is privacy-preserving because speedometer measurements do not have physical locations. However, we show that with knowledge of the user's home location, as the insurance companies have, speed data is sufficient to discover driving routes and destinations when trip data is collected over a period of weeks. To demonstrate the real-world applicability of our approach we applied our algorithm, elastic pathing, to data collected over hundreds of driving trips occurring over several months. With this data and our approach, we were able to predict trip destinations to within 250 meters of ground truth in 10% of the traces and within 500 meters in 20% of the traces. This result, combined with the amount of speed data that is being collected by insurance companies, constitutes a substantial breach of privacy because a person's regular driving pattern can be deduced with repeated examples of the same paths with just a few weeks of monitoring.

Yang, Y., Yuan, W., Hou, J., & You, Z. (2022). Review on Physical and Chemical Factors Affecting Fines Migration in Porous Media. Water Research, 118172. https://doi.org/10.1016/j.watres.2022.118172

Yang, Y., Yuan, W., Hou, J., & You, Z. (2022). Review on Physical and Chemical Factors Affecting Fines Migration in Porous Media. Water Research, 118172. https://doi.org/10.1016/j.watres.2022.118172