Quantum Computing in Cybersecurity

23Quantum Computing and Security Aspects of Attention-Based Visual Question Answering with Long Short-Term Memory

Madhav Shrivastava¹*, Rajat Patil², Vivek Bhardwaj³, Romil Rawat⁴, Shrikant Telang⁵ and Anjali Rawat⁶

¹Tata Consultancy Services (TCS), Indore, India

²Department of Computer Science Engineering, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India

³Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

⁴Department of Computer Science and Engineering, SVIIT, SVVV, Indore, India

⁵Department of Information Technology, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India

⁶Apostelle Overseas Education, Ujjain, India

Abstract

In this write-up, we will study the core concept of VQA the LSTM with Att.-based models and CNN Att. models that combine the local images’ hidden features and the answer of the question which is raised by end user is produced from the portion of the image which is generated by image dataset. So, the word Att. means that it only keeps Att. on those parts which are relevant to both object and keywords in the question. We are not considering the outlier to reduce the chances of mistakes. To combine the results from the image and given questions we are using multi-layer awareness.

In this proposal of QC in field of VQA and LSTM, we tried to use this concept of MM Networks and presented our view on vulnerability of a primary/novel kind of attack that we call as DKMB. This hard kind of theft breaks the ...