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Abstract Image captioning aims to describe the content of images with a sentence. It is a natural way for people to express their understanding, but a challenging and important task from the view of image understanding. In this paper, we propose two innovations to improve the performance of such a sequence learning problem. First, we give a new attention method named triple attention (TA-LSTM) which can leverage the image context information at every stage of LSTM. Then, we redesign the structure of basic LSTM, in which not only the stacked LSTM but also the paralleled LSTM are adopted, called as PS-LSTM. In this structure, we not only use the stack LSTM but also use the parallel LSTM to achieve the improvement of the performance compared with the normal LSTM. Through this structure, the proposed model can ensemble more parameters on single model and has ensemble ability itself. Through numerical experiments, on the public available MSCOCO dataset, our final TA-PS-LSTM model achieves comparable performance with some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A novel method that facial expression saliency maps are proposed to generate facial expression adversarial examples. • The OMIM is proposed to help facial expression saliency maps accelerate to generate facial expression adversarial examples. • Our method is superior to other attack methods, such as FGSM, PGD, BIM, CW. The security demands in humanization should be considered as important, because artificial intelligence is developing rapidly in this area. Recent studies have shown the vulnerability of many deep learning models to adversarial examples; however, only a few studies on facial expression adversarial examples have been conducted. Thus, in this paper, we propose a novel method for generating facial expression adversarial examples using facial saliency maps and facial masking maps. Extensive numerical experiments demonstrate the outstanding performance of our method in terms of attack accuracy, structural similarity index measure score, and computational time, compared with other leading methods, such as the fast gradient sign method, projected gradient descent method, and Carlini–Wagner attacks. [ABSTRACT FROM AUTHOR]