Securitylab•October 9, 2026•🇷🇺Translated from Russian

Why AI Detectors Cannot Be Trusted: The Shift to Watermarks and C2PA Standards

Detecting AI-generated images by examining fingers, teeth, or text has become ineffective as modern generators now produce realistic hands, photographic simulations, and synthetic voices. Visual or audio inspection therefore offers only grounds for suspicion rather than reliable proof. Manufacturers are shifting from post-factum detection to embedding machine-readable provenance signals directly into created files.

In 2026 the process accelerated sharply. From August 2 the European Union began enforcing Article 50 of the AI Act on AI-content transparency. Providers of generative systems must ensure machine-readable labeling of synthetic images, video, audio, and text, subject to defined exceptions. Against this regulatory backdrop Anthropic is rolling out watermarks in Claude, Google is expanding SynthID, OpenAI launched Verify, and native checkers appeared at Midjourney, Meta, and ElevenLabs.

No universal “check any file for any AI” button exists. OpenAI Verify primarily recognizes its own signals, Gemini searches for SynthID from Google models, Claude looks for its own signature, and Midjourney checks its proprietary mark. Even shared technology does not guarantee detector compatibility. Google explicitly warns that Gemini does not yet recognize SynthID from other vendors, although SynthID is already adopted by OpenAI, Apple, ElevenLabs, NVIDIA and others.

What can be detected inside a file

Modern verification systems rely on three fundamentally different mechanisms. The first involves C2PA and Content Credentials, which store cryptographically signed information about a file’s origin and editing history. The second mechanism is an invisible watermark embedded directly into the image, audio waveform, video, or statistical structure of text. The third mechanism uses statistical classifiers that analyze content without any pre-embedded marker and estimate similarity to known model outputs.

C2PA cannot be treated as a simple “made by AI” seal. The standard is also used by cameras, editors, and conventional publishing systems. A correctly signed C2PA record confirms provenance and integrity but says nothing about the truthfulness of the depicted scene. The C2PA consortium itself warns that origin data is not a fact-check.

Invisible watermarks such as Google SynthID, Meta Content Seal, and Anthropic text watermarks are designed to survive compression and resizing, yet no watermark is indestructible. Independent tests have already shown that aggressive cropping can defeat detection.

Statistical classifiers require no prior marker but remain probabilistic. They struggle especially with human-written text later edited by models. Modern detectors have improved, yet studies continue to record false positives, misses, and sharp score changes after minor editing.

Which services check what

The table of available tools illustrates the market’s core limitation: most official detectors answer only narrow questions such as “does this image contain an OpenAI signal?” rather than the broad question “was this file created by any AI?”

  • OpenAI Verify accepts images and audio and looks for C2PA plus SynthID from OpenAI; it does not detect other vendors.
  • Gemini checks images, video, and audio for SynthID Google and Content Credentials but does not recognize SynthID from other companies.
  • Claude Check Content searches for C2PA records from Claude and performs checks locally in the browser.
  • Midjourney Verify looks for a hidden Midjourney identifier that is easily lost after re-saving or social-media upload.
  • Content Credentials Verify reads any valid C2PA metadata across images, video, audio, and PDF.
  • ElevenLabs Audio Detector primarily searches for SynthID ElevenLabs and falls back to a classifier.
  • Meta Content Seal Detector checks images from Muse Image for the Content Seal watermark.

OpenAI Verify supports PNG, JPG, WebP, MP3, WAV and other formats up to defined size limits. Positive results indicate the file was created or processed with supported OpenAI tools. Negative results may simply mean an older file, removed metadata, or another generator was used.

Google integrated checking directly into Gemini. Authorized users can upload files up to 100 MB; video is limited to under 90 seconds and audio to under one hour. Gemini combines SynthID with Content Credentials and can highlight video segments containing the watermark.

Anthropic’s public Claude Check Content page accepts numerous image and audio formats up to 100 MB and performs verification locally. Text watermark detection for newer Claude models is available only through a closed Detection API for qualified organizations.

Midjourney Verify accepts PNG, JPEG, WebP, GIF and TIFF up to 20 MB without requiring an account. The company places a hidden identifier in every generated image, yet warns that screenshots and most social platforms remove the mark.

ElevenLabs Audio Detector first looks for SynthID and falls back to a classifier. Files created before June 2026 lack the new watermark.

Meta’s preliminary Content Seal Detector demonstrated both utility and weakness: Reuters testing showed that cropping images to roughly half or one-third of original area defeated detection in 55 percent of cases.

Apple, Stability AI, and Microsoft also participate in the SynthID or C2PA ecosystems. xAI uses a visible “Grok” label on generated images and video.

Text remains the hardest medium

Plain text files lose conventional metadata upon copying. Anthropic therefore embeds statistical watermarks directly into word selection for models released after August 2, 2026. Google has developed a textual variant of SynthID. Public cross-vendor text detectors still do not exist.

Services such as Pangram and GPTZero rely on statistical analysis. Results from different systems often diverge, mixed human-AI text is especially difficult, and minor editing can dramatically alter scores. A “96 percent AI” reading does not equal cryptographic proof that 96 percent of the words were written by ChatGPT.

How to check a suspicious file correctly

The most useful step is obtaining the original file rather than a screenshot or social-media copy. Recommended procedure:

  • Obtain the original file whenever possible.
  • Identify the suspected generator and test with its official checker first.
  • Check C2PA via Content Credentials Verify.
  • Compare multiple independent signals rather than averaging classifier percentages.
  • Never treat a negative result as conclusive proof of human origin.
  • Look outside the file: reverse image search, original publication date, EXIF data, and version history frequently reveal more than any detector.

The strongest evidence occurs when an original file contains both a trusted C2PA record from a specific vendor and a matching invisible watermark. Even then, the result does not prove content truthfulness, authorship, or the exact proportion of AI work.

Related articles

Habr•AI Security

Local LLMs in AppSec: What Open Tools and Models Can Handle in Application Security

The article examines how local large language models can be integrated into application security workflows without relying on cloud subscriptions. It traces the evolution from llama.cpp in 2023 through agentic frameworks like PentAGI and Strix in 2025-2026. Teams can now run models on local hardware, connect them to code analyzers such as Semgrep, and delegate specific tasks like vulnerability path reconstruction or multi-step application testing. The piece highlights practical scenarios including static code review, dynamic testing with browser and terminal agents, and security evaluation of embedded AI assistants using tools like Garak and PyRIT. It stresses the importance of task decomposition, context management, and verification of model outputs to avoid misleading results. Hardware performance, model selection, and custom agent configurations are presented as key factors for successful adoption.

安全客•AI Security

From Accessible to Controllable: Ruishu Information Launches AITM for AI Agent Lifecycle Security

As AI Agents become digital employees in enterprise R&D, office, and business workflows, organizations must now ensure they are manageable, controllable, and auditable. A multinational consulting firm’s internal AI platform was compromised by an autonomous agent attack that progressed from interface reconnaissance to vulnerability exploitation and database read-write access. Conventional scanners missed the injection points, but the agent inferred query structures from database error messages. Ruishu Information has responded with its Agent Intelligent Lifecycle Management system (AITM), which enforces security across admission, runtime, permissions, and data flows. The platform combines static scanning, behavioral analysis, semantic judgment, and sandbox execution for pre-deployment checks, followed by continuous runtime validation and LLM data-plane protection. AITM applies HMAC-SHA256 signatures, tool whitelists, PII tokenization, and four-stage Trace auditing to reduce blind spots throughout the agent lifecycle.

BoletimSec•AI Security

South Korean Hacker Uses Generative AI Framework ARTEX to Breach 90 Organizations and Steal 8.2 Million Records

Seoul Metropolitan Police arrested a 32-year-old man identified only as A for conducting a series of intrusions against approximately 90 South Korean companies and institutions between September 2025 and October 2026. The attacker, who had no formal technology education and learned hacking from publicly available online materials, previously worked in penetration testing at a security firm from 2019 to 2022. Roughly 60 of the victims were compromised using the generative AI-powered pentesting framework ARTEX, which autonomously determined attack paths after receiving only the target name. The operation resulted in the theft of 8.2 million personal records and internal documents, after which the perpetrator extorted 38 organizations for a total of 360 million won in Tether, ultimately receiving 300,000 won. Police recovered part of the stolen material and are investigating possible copies on the dark web, classifying the case as the country's first large-scale incident involving generative AI. Six additional suspects remain under investigation while the main perpetrator has been referred to prosecutors on charges of violating information and communications network laws and extortion.

Habr•AI Security

AI in Vulnerability Management: Assistant, Weapon, and Emerging Target

Artificial intelligence now plays three simultaneous roles in vulnerability management: it assists defenders with prioritization and analysis, arms attackers with faster discovery tools, and introduces entirely new classes of assets and vulnerabilities that must be managed. Mature applications include ML-driven risk scoring in platforms such as Tenable VPR, Qualys TruRisk and Rapid7 Active Risk, which cut priority lists by 40-50 percent, and Google’s Big Sleep project that discovered a real-world SQLite flaw (CVE-2025-6965) before exploitation occurred. On the offensive side, systems like XBOW completed 104 test scenarios in 28 minutes versus 40 hours for human testers, shrinking the average time-to-exploit window to negative seven days according to Mandiant M-Trends 2025. New AI-specific attack surfaces are documented in the OWASP Top 10 for LLM applications and the emerging MCP Top 10, covering prompt injection, tool poisoning, and command injection against Model Context Protocol servers. Real incidents already include Air Canada’s chatbot inventing a refund policy and New York City’s MyCity bot dispensing illegal advice for nearly two years. Researchers have also demonstrated sandbox escapes in coding agents from Cursor, OpenAI Codex and Claude Code through classic file-write-and-execute patterns rather than any sentient behavior.