AdFlush

Original Article ↗ Hacker News Discussion ↗

AdFlush approach and results

  • AdFlush uses a classical, feature-engineered ML model to detect ad-related resources (e.g., JS AST structure, identifier length, access patterns, graph metrics of scripts).
  • Reported F1 score is 0.98 on 10,000 real sites, outperforming prior academic systems (AdGraph, WebGraph, WTAgraph).
  • It is advertised as more CPU- and memory-efficient than those systems and more robust to adversarial manipulation.

Comparison with uBlock Origin and list-based blockers

  • Several commenters ask why comparison is not primarily against popular list-based blockers.
  • The paper does contain a uBlock Origin comparison: AdFlush F1 ≈ 0.86 vs uBO ≈ 0.84, a marginal advantage that is not claimed to be statistically significant.
  • Many view list-based blocking as a “solved” practical solution; algorithmic approaches are seen as more about research value or long-term robustness.

Performance and real-world viability

  • Significant performance penalty: median page load times reported as ~2.7s (no blocker), ~2.1–2.2s (uBO), 6.6s (AdFlush fresh), 3.4s (AdFlush with cached predictions).
  • Commenters doubt it can compete with URL-rule matching for real-time use but see potential as an offline tool to generate/augment filter lists.

Crowdsourced lists: strengths, weaknesses, and abuse

  • Lists scale well: one report can protect millions.
  • Concerns raised about abuse, “corruption,” and pay-to-whitelist/blacklist behaviors, as well as accidental overblocking and the difficulty of getting removed.
  • Others argue abuses and mistakes are observable and often quickly reversible.

Chrome Manifest V3 and ecosystem concerns

  • Discussion centers on MV3’s limits: only declarative rules, no dynamic inspection, bans on remote code, and capped rule counts (with mentions of recent increases).
  • Many see MV3 as aimed at weakening powerful adblockers, potentially breaking advanced or algorithmic blockers entirely.
  • Some predict a browser exodus (to Firefox, Brave, etc.) if adblocking becomes ineffective; others think most users won’t switch.

Hybrid and future directions

  • Multiple commenters propose hybrid systems: lists for fast blocking; ML for offline analysis or catching evasive ads.
  • Ideas extend to network-layer/MITM filtering, DNS-level tools (Pi-hole, AdGuard Home) plus browser blockers, and even AI-based page rendering that strips ads before display.
  • Native ads and server-side-integrated ads are flagged as the hardest to handle; some see them as unavoidable without changing what content users choose to consume.