Short answer

• Antidetect browsers can reduce the chance that two accounts get linked by simple browser fingerprinting and cookie-based signals, but they do not reliably prevent linking by a determined, well-resourced operator. They are a partial, risk-reduction tool — not a guarantee of unlinkability.

Why they help (what they change)

• Fingerprint control: they let you present different combinations of user‑agent, screen size, timezone, language, fonts, canvas/webgl values, and other JS-exposed attributes so website fingerprint hashes look different across profiles.
• Profile compartmentalization: isolated storage (cookies, localStorage, IndexedDB) per “profile” prevents easy cross-site cookie leaks between profiles.
• Network integration: many support built‑in proxies/VPNs (per-profile) so IP/geolocation can be separated from profile identity.
• Automation resistance settings: they can hide or modify things like webdriver flags, navigator.permissions, or certain headers that naive bot-detection looks for.

How operators actually link accounts (what antidetects don’t fully cover)

• Network-level correlation: same IP ranges, ISP behavior, carrier-grade NAT patterns, or shared proxy/VPN infrastructure can be correlated. Also timing and scheduling of requests.
• Account signals outside the browser: identical phone numbers, emails, payment methods, shipping addresses, or social graphs linking accounts are definitive.
• Cross-device identifiers: mobile device IDs, installed apps, or OS-level identifiers won’t be changed by a browser profile.
• Advanced fingerprinting: high-entropy signals such as GPU rendering micro‑differences, audio/context fingerprinting, high-resolution canvas/webgl quirks, CPU timing patterns, and clock skew can still correlate profiles if not perfectly emulated.
• Server-side analytics and ML: large services use behavioral patterns (typing/mouse dynamics, navigation paths, session timing, order-of-actions) and ML models that spot statistically improbable differences or similarities across sessions.
• Persistent browser storage outside profile: system-level caches, enterprise management software, or browser extensions can leak cross-profile data.
• Human/logging correlation: customer support interactions, password resets, or fraud investigations use many signals together (not just browser fingerprint) to link accounts.

Realistic effectiveness

• Against casual linking (simple fingerprint or same browser + cookies), well-configured antidetects are often effective.
• Against modern anti-fraud systems of major platforms (social networks, marketplaces, banks), antidetects only raise the bar — they do not give strong, provable unlinkability.
• Against determined investigators (platform security teams, law enforcement, forensic analysts), antidetects are unreliable as a sole defense.

Risks and common failure modes

• Misconfiguration (reused proxied IPs, inconsistent timezone vs. locale, mismatched fonts vs reported OS) makes profiles look fake and increases suspicion.
• Using low-quality or shared proxy pools that many others use creates linking risk.
• Relying on browser-only measures while reusing email/phone/payment defeats the purpose.
• Legal/terms-of-service risk: many services ban use of such tools and may suspend accounts or take legal action in fraud cases.
• Malware/compromised antidetect builds: third‑party or cracked versions can include backdoors that reveal real identifiers.

If your goal is legitimate privacy (recommended approach)

• Use compartmentalization: different OS users/VMs and separate networks (different residential proxies or cellular hotspots).
• Keep identity signals separate: unique emails, phone numbers (if required, use legitimate, platform-allowed numbers), payment methods, addresses.
• Match profile attributes consistently: locale, fonts, timezone, keyboard layout, and installed fonts should plausibly align with the claimed identity/location.
• Harden behavioral separation: different usage patterns, distinct browsing histories, separate contact lists.
• Prefer reputable tools and open-source privacy solutions; avoid shady downloads.

If your goal is to evade fraud detection (warning)

• I can’t assist with planning or facilitating wrongdoing. Evading fraud detection, creating fake accounts for malicious purposes, impersonation, or other illicit activity is illegal and unethical. If your use case is legitimate (testing, privacy, research), follow platform terms and laws and consider coordinating with the platform (e.g., authorized pen testing).

Practical checklist for better (ethical) compartmentalization

1. Use separate VMs or physical devices per identity when possible.
2. Use high-quality, dedicated residential proxies or cellular data per profile.
3. Ensure each profile’s OS, fonts, timezone, language, screen resolution, and GPU fingerprint are internally consistent.
4. Don’t reuse emails, phone numbers, payment methods, or shipping addresses.
5. Keep browser storage and extensions isolated.
6. Introduce distinct behavioral patterns (different browsing history, login times, interaction styles).
7. Monitor for leaks: check for shared cookies, supercookies, or third‑party trackers that can cross-link.
8. Use reputable, updated tools and keep software patched.

Bottom line Antidetect browsers are useful for reducing simple fingerprint-based linking, but they are not a silver bullet. Strong linking prevention requires a holistic approach (network, device, identity, and behavior) and even then cannot guarantee anonymity against sophisticated detection or legal investigation. If your aim is legitimate privacy, combine technical compartmentalization with lawful practices; if your aim is to commit or enable fraud, stop — I won’t help with that.