Antonio Joia Neto

Antonio Joia Neto, Amarin Amarin, Norrathep Rattanavipanon et al.

Trusted Execution Environments (TEEs) on low-power microcontrollers (e.g., ARM TrustZone-M) enable isolation of Secure and Non-Secure software but still require both worlds to share resources, including interrupt controllers. In this model, real-time applications and real-time operating systems (RTOS-s) are executed in the Non-Secure sub-system, whereas the Secure sub-system is typically reserved for a small set of pre-defined security (e.g., cryptographic) operations referred to as trusted computing services. However, many RTOS-s rely on periodic interrupts (SysTicks) to advance their own notion of time (time-keeping), and the delivery of this interrupt is essential for preserving real-time behavior. On the other hand, the security of many trusted computing services requires atomicity vis-a-vis the Non-Secure sub-system (where the RTOS resides), precluding SysTick handling. This paper first characterizes this conflict and then introduces a Secure-driven time synchronization mechanism in which the Secure World measures elapsed time and compensates the Non-Secure RTOS by unobtrusively updating the RTOS time-keeping data structures with the appropriate number of missed ticks before re-enabling interrupts and resuming the execution of the Non-Secure system. This approach restores a consistent, monotonic notion of time across worlds and enables secure coexistence of trusted computing services and RTOS-s on microcontrollers. Importantly, the proposed approach requires no modifications to the underlying RTOS and yields no significant run-time overhead.

Gustavo Sutter P. Carvalho, Diogo C. Luvizon, Antonio Joia Neto et al.

Generating static novel views from an already captured image is a hard task in computer vision and graphics, in particular when the single input image has dynamic parts such as persons or moving objects. In this paper, we tackle this problem by proposing a new framework, called CycleMPI, that is capable of learning a multiplane image representation from single images through a cyclic training strategy for self-supervision. Our framework does not require stereo data for training, therefore it can be trained with massive visual data from the Internet, resulting in a better generalization capability even for very challenging cases. Although our method does not require stereo data for supervision, it reaches results on stereo datasets comparable to the state of the art in a zero-shot scenario. We evaluated our method on RealEstate10K and Mannequin Challenge datasets for view synthesis and presented qualitative results on Places II dataset.

Antonio Joia Neto, Andre G C Pacheco, Diogo C Luvizon

In this paper, we propose a new Sound Event Classification (SEC) method which is inspired in recent works for out-of-distribution detection. In our method, we analyse all the activations of a generic CNN in order to produce feature representations using Gram Matrices. The similarity metrics are evaluated considering all possible classes, and the final prediction is defined as the class that minimizes the deviation with respect to the features seeing during training. The proposed approach can be applied to any CNN and our experimental evaluation of four different architectures on two datasets demonstrated that our method consistently improves the baseline models.