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Ionic polymer-metal composite (IPMC), which is one of the electro-active polymer actuators, is expected as artificial muscles for robots. An interesting property of IPMC is that it requires water to work, therefore it is suitable for underwater robots. In this paper, we developed an underwater robot which mimics rajiform swimming, that is the swimming form of a ray fish. Fins are designed using sixteen IPMCs. For autonomous operation, miniaturization of the electrical devices such as a micro controllers and small amplifiers are performed. A simple traveling wave control input is employed to generate moment on the fin. In the experiment, propulsion speed is measured under various control parameters. Furthermore, incremental wave of the fin is observed although the amplitude of the control input is spatially uniform. We also discuss this phenomenon from the point of view of interaction between elasticity of the actuator and fluid dynamics.

This paper presents a scrambling inversion attack using a generative adversarial network (SIA-GAN). This method aims to evaluate the privacy protection level achieved by image scrambling method. For privacy-preserving machine learning, scrambled images are often used to protect visual information, assuming that searching the scramble parameters is highly difficult for an attacker due to the application of complex image scrambling operations. However, the security of such methods has not been thoroughly investigated. SIA-GAN learns the mapping between pairs of scrambled images and original images, then attempts to invert image scrambling. Therefore, the attacker is assumed to have real images whose domain is the same as that of scrambled images. Experimental results demonstrate that scrambled images cannot be recovered if block shuffling is applied as a scrambling operation. The experimental code of SIA-GAN is publicly available after the paper is accepted.

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