AI phishing & spam detection

Traditional phishing was detectable by its flaws: template reuse, crude forgeries, and language errors that filters and users learned to catch. AI-generated phishing removes the flaws and adds variation — every message and forged document can be unique, defeating signature and hash-based blocking, and lures arrive as convincing images and PDFs rather than suspicious text. Detection shifts accordingly: instead of matching known-bad patterns, filters need forensic signals on whether the media was generated or manipulated — the layer ScamAI adds.

Forged invoices carry detectable traces even when they look perfect: editing artifacts, font and layout inconsistencies, and signs of AI generation. Automated document forensics finds these at scale — every attachment scored as it arrives, not left to an accounts-payable clerk to notice. ScamAI's REST API returns a per-document result with evidence, which mail security stacks consume as another signal: a message with a synthetic invoice gets quarantined or flagged even when its text and sender pass every other check.

Image-based phishing puts the deception inside media instead of text: a screenshot styled as a security alert, a QR code replacing a clickable link, a payment instruction rendered as a picture. Filters tuned to message text and URLs miss it because such messages carry minimal, innocuous text — the attack lives in pixels the classifier never reads. Countering it requires analyzing the media for manipulation and synthetic origin — a media forensics signal alongside conventional filtering closes the gap.