Visual-Based Spam Filtering for Obfuscated Emails
Paper#Spam Detection, Computer Vision, Machine Learning🔬 Research|Analyzed: Jan 3, 2026 16:01•
Published: Dec 29, 2025 18:18
•1 min read
•ArXivAnalysis
This paper addresses the growing problem of spam emails that use visual obfuscation techniques to bypass traditional text-based spam filters. The proposed VBSF architecture offers a novel approach by mimicking human visual processing, rendering emails and analyzing both the extracted text and the visual appearance. The high accuracy reported (over 98%) suggests a significant improvement over existing methods in detecting these types of spam.
Key Takeaways
- •Addresses the problem of spam emails using visual obfuscation.
- •Proposes a novel visual-based spam detection architecture (VBSF).
- •Employs a multi-step process mimicking human visual processing.
- •Combines OCR, Naive Bayes, Decision Trees, and CNNs.
- •Achieves high accuracy (over 98%) on the designed dataset.
Reference / Citation
View Original"The VBSF architecture achieves an accuracy of more than 98%."