Why Does Little Robustness Help? Understanding Adversarial Transferability From Surrogate Training

Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable AEs, many of these findings lack explanations and even lead to inconsistent advice. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing little robustness phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates, we attribute it to a trade-off between two predominant factors: model smoothness and gradient similarity. Our investigations focus on their joint effects, rather than their separate correlations with transferability. Through a series of theoretical and empirical analyses, we conjecture that the data distribution shift in adversarial training explains the degradation of gradient similarity. Building on these insights, we explore the impacts of data augmentation and gradient regularization on transferability and identify that the trade-off generally exists in the various training mechanisms, thus building a comprehensive blueprint for the regulation mechanism behind transferability. Finally, we provide a general route for constructing better surrogates to boost transferability which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the crucial role of manipulating surrogate models.Comment: Accepted by IEEE Symposium on Security and Privacy (Oakland) 2024; 21 pages, 11 figures, 13 table

Efficacy and safety of Tai Chi for Parkinson's disease: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND AND OBJECTIVE:In Parkinson's disease (PD), wearing off and side effects of long-term medication and complications pose challenges for neurologists. Although Tai Chi is beneficial for many illnesses, its efficacy for PD remains uncertain. The purpose of this review was to evaluate the efficacy and safety of Tai Chi for PD. METHODS:Randomized controlled trials (RCTs) of Tai Chi for PD were electronically searched by the end of December 2013 and identified by two independent reviewers. The tool from the Cochrane Handbook 5.1 was used to assess the risk of bias. A standard meta-analysis was performed using RevMan 5.2 software. RESULTS:Ten trials with PD of mild-to-moderate severity were included in the review, and nine trials (n = 409) were included in the meta-analysis. The risk of bias was generally high in the blinding of participants and personnel. Improvements in the Unified Parkinson's Disease Rating Scale Part III (mean difference (MD) -4.34, 95% confidence interval (CI) -6.67--2.01), Berg Balance Scale (MD: 4.25, 95% CI: 2.83-5.66), functional reach test (MD: 3.89, 95% CI: 1.73-6.04), Timed Up and Go test (MD: -0.75, 95% CI: -1.30--0.21), stride length (standardized MD: 0.56, 95% CI: 0.03-1.09), health-related quality of life (standardized MD: -1.10, 95% CI: -1.81--0.39) and reduction of falls were greater after interventions with Tai Chi plus medication. Satisfaction and safety were high. Intervention with Tai Chi alone was more effective for only a few balance and mobility outcomes. CONCLUSIONS:Tai Chi performed with medication resulted in promising gains in mobility and balance, and it was safe and popular among PD patients at an early stage of the disease. This provides a new evidence for PD management. More RCTs with larger sample size that carefully address blinding and prudently select outcomes are needed. PROSPERO registration number CRD42013004989

The effect of Tai Chi on stride length.

<p>Subgroup analysis was performed according to whether or not medications were included in the intervention. Standardized mean difference was used for different units of stride length.</p

The effect of Tai Chi on gait velocity.

<p>Subgroup analysis was performed according to whether or not medications were included in the intervention. Standardized mean difference was used for different units of velocity.</p

The effect of Tai Chi on UPDRS III score.

<p>Subgroup analysis was performed according to whether or not medications were included in the intervention. A random model was used to address the high heterogeneity. UPDRS III, Unified Parkinson's Disease Rating Scale Part III.</p