Sink or SWIN? Image Similarity Search with ViT to Stop Phishing Attacks

Presented by

Adithya Singh, Machine Learning Engineer at Bolster

About this talk

Vision Transformers are quickly emerging as the top method in solving computer vision tasks for anti-phishing use cases due to the immense success of transformers in the NLP domain. For the longest time, CNNs and hashing systems have been go-to methods to solve image classification problems. However, many limitations of these methods have given rise to powerful vision transformers like SWIN which is taking the field of computer vision by storm. Join us as we do a technical deep dive into how image similarity search to solve image classification and remarkable capabilities of SWIN transformers, which leverage the power of self-supervised learning and visual recognition to identify sophisticated phishing campaigns. Adithya Singh, Machine Learning Engineer at Bolster, will discuss how SWIN transformers can be used for: - Analyzing complex visual cues for threat categorization - Detecting subtle manipulations in the visual data - Distinguishing with high fidelity legitimate websites from malicious ones; - and being added to your existing tools for phishing and scam detection
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