These days, a lot of videos are uploaded onto web-based video sharing services such as YouTube. These videos can be freely accessed from all over the world. On the other hand, they often contain the appearance of walking private people, which could be identified by silhouette-based gait recognition techniques rapidly developed in recent years. This causes a serious privacy issue. To avoid it, this paper proposes a method for anonymizing the appearance of walking people, namely human gait, in video. In the proposed method, we first crop human regions from all frames in an input video and binarize them to get their silhouettes. Next, we slightly deform the silhouettes from the aspects of static body shape and dynamic walking rhythm so that the person in the input video cannot be correctly identified by gait recognition techniques. After that, the textures of the original human regions are transferred onto the deformed silhouettes. We achieve this by a displacement field-based approach, which is training-free and thus robust to a variety of clothes. Finally, the anonymized human regions with the transferred textures are filled back into the input video. In the results of our experiments, we successfully degraded the accuracy of CNN-based gait recognition systems from 100% to 1.57% in the lowest case without yielding serious distortion in the appearance of the human regions, which demonstrated the effectiveness of the proposed method.

Cybersecurity in front of attacks to a face recognition system is an emerging issue in the cloud era, especially due to its strong bonds with the privacy of the users registered to the system. A possible attack is the model inversion attack (MIA) which aims to reveal the identity of a targeted user by generating the most proper datapoint input to the system with maximum corresponding confidence score at the output. The generated data of a registered user can be maliciously used as a serious invasion of the user privacy. In literature, MIA processes are categorized into white-box and black-box scenarios which are respectively with and without information about the system structure, parameters, and partially about the users. This research work assumes the MIA under semi-white box scenario of availability of system model structure and parameters but not any user data information, and verifies it as a severe threat even for a deep-learning-based face recognition system despite its complex structure and the diversity of registered user data. The alert state is promoted by Deep MIA which is the integration of deep generative models in MIA, and$\alpha $-GAN integrated MIA-initilized by a face based seed ($\alpha $-GAN-MIA-FS) is proposed. As a novel MIA search strategy, a pre-trained deep generative model with capability of generating a face image from a random feature vector is used for narrowing down the image search space to the feature vectors space, which has much lower dimensions. This allows the MIA process to efficiently search for a low-dimensional feature vector whose corresponding face image maximizes the confidence score. We have experimentally evaluated the proposed method by two objective criteria and three subjective criteria in comparison to$\alpha $-GAN-integrated MIA initialized with a random seed ($\alpha $-GAN-MIA-RS), DCGAN-integrated MIA (DCGAN-MIA), and the conventional MIA. The evaluation results approve the efficiency and superiority of the proposed technique in generating natural looking face clones with high recognizability as the targeted users.

International audience

Cell stiffness could be an index for evaluating its activity. Although various systems measuring cell stiffness have been proposed so far, they are slow for adaptively connecting to cell sorters capable of handling more than 1000 [cells/sec]. This paper proposes a new approach that can indirectly evaluate the cell stiffness by measuring the passing time for a narrow channel. When a cell passes through the channel, it receives a viscous force depending upon how much deformation is exerted on the cell. We show that the stiffness is a function of both the passing time and the initial diameter of cell. We also show that the stiffness is proportional to the passing time and inversely proportional to the initial diameter, under the assumption that the thickness of fluid film is inversely proportional to the normal force. The experimental validation is given together with the basic working principle.

This paper investigates how hearers cope with a sequence of more than two identical vowels --a common occurrence in Japanese speech. In the segmentation of identical vowels there are no spectral cues and very small power envelope change in usual utterances containing identical vowels. We consider the effects of suprasegmental information such as duration, pitch pattern and rhythm of speech as important cues, and examine how, and to what extent a hearer can successfully make use of such information to segment each mora in a consecutive vowel series with and without the preceding sentential context.