Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks

Transfer-based adversarial attacks can effectively evaluate model robustness in the black-box setting. Though several methods have demonstrated impressive transferability of untargeted adversarial examples, targeted adversarial transferability is still challenging. The existing methods either have low targeted transferability or sacrifice computational efficiency. In this paper, we develop a simple yet practical framework to efficiently craft targeted transfer-based adversarial examples. Specifically, we propose a conditional generative attacking model, which can generate the adversarial examples targeted at different classes by simply altering the class embedding and share a single backbone. Extensive experiments demonstrate that our method improves the success rates of targeted black-box attacks by a significant margin over the existing methods -- it reaches an average success rate of 29.6\% against six diverse models based only on one substitute white-box model in the standard testing of NeurIPS 2017 competition, which outperforms the state-of-the-art gradient-based attack methods (with an average success rate of $<$2\%) by a large margin. Moreover, the proposed method is also more efficient beyond an order of magnitude than gradient-based methods

Manual discrimination of force

Optimal design of human-machine interfaces for teleoperators and virtual-environment systems which involve the tactual and kinesthetic modalities requires knowledge of the human's resolving power in these modalities. The resolution of the interface should be appropriately matched to that of the human operator. We report some preliminary results on the ability of the human hand to distinguish small differences in force under a variety of conditions. Experiments were conducted on force discrimination with the thumb pushing an interface that exerts a constant force over the pushing distance and the index finger pressing against a fixed support. The dependence of the sensitivity index d' on force increment can be fit by a straight line through the origin and the just-noticeable difference (JND) in force can thus be described by the inverse of the slope of this line. The receiver operating characteristic (ROC) was measured by varying the a priori probabilities of the two alternatives, reference force and reference force plus an increment, in one-interval, two-alternative, forced-choice experiments. When plotted on normal deviate coordinates, the ROC's were roughly straight lines of unit slope, thus supporting the assumption of equal-variance normal distributions and the use of the conventional d' measure. The JND was roughly 6-8 percent for reference force ranging from 2.5 to 10 newtons, pushing distance from 5 to 30 mm, and initial finger-span from 45 to 125 mm. Also, the JND remained the same when the subjects were instructed to change the average speed of pushing from 23 to 153 mm/sec. The pushing was terminated by reaching either a wall or a well, and the JND's were essentially the same in both cases

Protective Effect of FTY720 on Several Markers of Liver Injury Induced by Concanavalin A in Mice

AbstractBackground2-Amino-2-[2-(4-octylphenyl)ethyl] propane-1,3-diol hydrochloride (FTY720) is a novel agent with protective effect on several markers of liver injury. It is a chemical substance derived by modifying myriocin from the ascomycete Isaria sinclairii. It has been reported that FTY720 is able to treat autoimmune encephalomyelitis, renal cancer, asthma, and multiple sclerosis. More potent clinical applications of FTY720 need to be investigated.ObjectiveThe aim of this study was to evaluate the protective effect of FTY720 on several markers of experimental liver injury and to investigate the possible mechanism of action.MethodsConcanavalin A (Con A) at a dose of 15 mg/kg was intravenously. injected in mice, and 10 days before the Con A challenge, 1 mg/kg, 3 mg/kg, and 6 mg/kg of FTY720 were administered to mice. The liver injury was monitored biochemically by measuring serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) and tumor necrosis factor-α (TNF-α) levels. TNF-α and nuclear factor-κB (NF-κB) in liver tissue were detected by Western blot analysis.ResultsFTY720, when administered intragastrically for 10 days in mice with Con A–induced liver injury, dose-dependently reduced serum ALT and AST and TNF-α levels. The differences were statistically significant (P ≤ 0.05). It was also found that FTY720 decreases TNF-α and NF-κB protein expression in liver tissue.ConclusionsFTY720 is able to improve several markers of Con A–induced liver injury in mice, including serum ALT, serum AST, TNF-α, and NF-κB, which might be at least in part related to its ability to reduce TNF-α/NF-κB cascade activity

On the uncertainty principle of neural networks

Despite the successes in many fields, it is found that neural networks are difficult to be both accurate and robust, i.e., high accuracy networks are often vulnerable. Various empirical and analytic studies have substantiated that there is more or less a trade-off between the accuracy and robustness of neural networks. If the property is inherent, applications based on the neural networks are vulnerable with untrustworthy predictions. To more deeply explore and understand this issue, in this study we show that the accuracy-robustness trade-off is an intrinsic property whose underlying mechanism is closely related to the uncertainty principle in quantum mechanics. By relating the loss function in neural networks to the wave function in quantum mechanics, we show that the inputs and their conjugates cannot be resolved by a neural network simultaneously. This work thus provides an insightful explanation for the inevitability of the accuracy-robustness dilemma for general deep networks from an entirely new perspective, and furthermore, reveals a potential possibility to study various properties of neural networks with the mature mathematical tools in quantum physics.Comment: 8 pages, 5 figure