Enhanced Image Security Using Classification in Adversarial Machine Learning with aes Based Grey wolf Algorithm
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901048641191936 |
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| author | Jahan, Tasneem Rai, Divyarth |
| author_facet | Jahan, Tasneem Rai, Divyarth |
| contents | <p><strong><span>Traditional image retrieval methods which use plain images suffer security risks in fields like medicine, military, space exploration, stocks and finance. Image classification using adversarial machine learning models are vital for enhancing security and detecting intrusion. This paper attempts to present a comparative study and highlights the potential of most promising models for efficient and effective retrieval with feature learning in image classification tasks. The best approaches can eventually strengthen its impact on the field for further implementation.<span> </span>The various machine learning models which could intercept adversarial attacks are classified with their results and advantages. Across social media websites and recommender systems, malicious advertisements are increasingly popular. The approaches discussed here are robust to classify the advertisement images as malicious or benign. This is a good strategy for ensuring smooth user experience and maintaining user security. </span></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15687083 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Enhanced Image Security Using Classification in Adversarial Machine Learning with aes Based Grey wolf Algorithm Jahan, Tasneem Rai, Divyarth Keywords - Image Classification, adversarial machine learning, image security, neural network <p><strong><span>Traditional image retrieval methods which use plain images suffer security risks in fields like medicine, military, space exploration, stocks and finance. Image classification using adversarial machine learning models are vital for enhancing security and detecting intrusion. This paper attempts to present a comparative study and highlights the potential of most promising models for efficient and effective retrieval with feature learning in image classification tasks. The best approaches can eventually strengthen its impact on the field for further implementation.<span> </span>The various machine learning models which could intercept adversarial attacks are classified with their results and advantages. Across social media websites and recommender systems, malicious advertisements are increasingly popular. The approaches discussed here are robust to classify the advertisement images as malicious or benign. This is a good strategy for ensuring smooth user experience and maintaining user security. </span></strong></p> |
| title | Enhanced Image Security Using Classification in Adversarial Machine Learning with aes Based Grey wolf Algorithm |
| topic | Keywords - Image Classification, adversarial machine learning, image security, neural network |
| url | https://doi.org/10.5281/zenodo.15687083 |