Amoral Digital Images Detection Using Deep Dual Attention Network and Inception

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Norouzi M.; Hassanpour H.; Ghanbari A.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Engineering Year:2025 | Volume:38 | Issue:4 Page(s): 725-734

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Amoral Digital Images Detection Using Deep Dual Attention Network and Inception

Author(s)

Norouzi M. | Hassanpour H. | Ghanbari A. | Issue Writer Certificate

Keywords

amoral images detection

attention mechanism

Deep Learning

Pornography

Inception layer

Abstract

The ever-increasing growth of amoral content in cyberspace has caused the idea of detecting such contents in images, then creating an alarm or limiters in different age ranges around the world. This content not only detects harmful visual items, but also it detects instances of abusing individuals. This study has represented a new approach to detect images with amoral content. The suggested model includes two deep models based on inception blocks and attention and residual based convolutional layer which acts with two different approaches: the first model is a five-class recognition model and the second one is an estimator model which maps the image into levels of inappropriateness between 0 and 1. Combining these two models by an aggregator based on first-order rules leads to representing a model which improved 2.1% detection precision in not suitable for work (NSFW) dataset and 2.3% on TI-UNRAM dataset in comparison with other state-of-the-art models. Our approach also shows promising results to model culturally based definition of NSFW, especially in Iran.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans