Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique

AbstractWhile outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements, which may encourage their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool $${360}^{\circ }$$ 360 ∘ Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 25,349, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th–18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.

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Deep Learning Technique Based Visually Impaired People Using YOLO V3 Framework Mechanism

2021 3rd International Conference on Signal Processing and Communication (ICPSC) ◽

10.1109/icspc51351.2021.9451710 ◽

2021 ◽

Author(s):

A. Balachandar ◽

E. Santhosh ◽

A. Suriyakrishnan ◽

N. Vigensh ◽

S. Usharani ◽

...

Keyword(s):

Deep Learning ◽

Visually Impaired ◽

Visually Impaired People ◽

Impaired People ◽

Learning Technique

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ExypnoSteganos - A smarter approach to steganography

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189879 ◽

2021 ◽

pp. 1-12

Author(s):

Gaurav Sarraf ◽

Anirudh Ramesh Srivatsa ◽

MS Swetha

Keyword(s):

Neural Network ◽

Deep Learning ◽

Data Compression ◽

Building Block ◽

Communication Techniques ◽

Systems Security ◽

Learning Technique ◽

Critical Problems ◽

Sporadic Cases ◽

Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

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