InnerEye: A Tale on Images Filtered Using Instagram Filters - How Do We Interact with them and How Can We Automatically Identify the Extent of Filtering?

被引：0

作者：

Rakib, Gazi Abdur ^{[1
]}

Adnin, Rudaiba ^{[1
]}

Bashir, Shekh Ahammed Adnan ^{[1
]}

Islam, Chashi Mahiul ^{[1
]}

Turza, Abir Mohammad ^{[1
]}

Manzur, Saad ^{[1
]}

Rashik, Monowar Anjum ^{[1
]}

Azad, Abdus Salam ^{[1
,2
]}

Chakraborty, Tusher ^{[3
]}

Rahaman, Sydur ^{[1
]}

Shikder, Muhammad Rayhan ^{[1
]}

Ahmed, Syed Ishtiaque ^{[4
]}

Al Islam, A. B. M. Alim ^{[1
]}

机构：

[1] Bangladesh Univ Engn & Technol, Dhaka, Bangladesh

[2] Univ Calif Berkeley, Berkeley, CA 94720 USA

[3] Microsoft, Redmond, WA USA

[4] Univ Toronto, Toronto, ON, Canada

来源：

MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING, NETWORKING AND SERVICES, MOBIQUITOUS 2022 | 2023年 / 492卷

关键词：

Images; Filters; Survey; Deep Learning; CONTEXT;

D O I：

10.1007/978-3-031-34776-4_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Even though digitally filtered images are taking over the Internet for their aesthetic appeal, general people often feel betrayed if they are dealt with filtered images. Our study, comprising a series of structured surveys over images filtered using Instagram filters, reveals that different people perceive the filtered images differently. However, people have a common need for an automated tool to help them distinguish between original and filtered images. Accordingly, we develop an automated tool named 'InnerEye', which is capable of identifying how far an image is filtered or not. InnerEye utilizes a novel analytical design of a Neural Network Model that learns from a diverse set of images filtered using Instagram filters. Rigorous objective and subjective evaluations confirm the efficacy of InnerEye in identifying the extent of filtering in the images.

引用

页码：494 / 514

页数：21

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