REPOGEO REPORT · LITE

00quebec/Synthid-Bypass

Default branch main · commit f56620aa · scanned 6/7/2026, 12:08:03 PM

GitHub: 755 stars · 113 forks

AI VISIBILITY SCORE

23 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface 00quebec/Synthid-Bypass, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README H1 and opening paragraph to clarify niche

Why:

CURRENT

# SynthID-Bypass

**Disclaimer:** This project is intended for educational and AI safety research purposes only. The tools and techniques described herein should not be used for malicious purposes, to circumvent copyright, or to misrepresent the origin of digital content. This proof of concept is presented as-is and without warranty.

**Try it 100% free on twotensors (enable "Remove Synthid"): https://twotensors.ai/## 1. Overview

This repository contains a proof-of-concept exploration into the robustness of Google's SynthID [2], a digital watermarking technology integrated into Nano Banana Pro and related Google image systems to help identify synthetic media.

COPY-PASTE FIX

# SynthID-Bypass: AI Safety Research on Digital Watermark Resilience

This repository presents a proof-of-concept for bypassing Google's SynthID, a digital watermarking technology used in AI-generated images. Intended for AI safety research, it explores the robustness of such watermarks and techniques for assessing their resilience in synthetic media. **Disclaimer:** This project is for educational and research purposes only and should not be used for malicious intent or to circumvent copyright.

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Add a LICENSE file (e.g., MIT, Apache-2.0, or a custom license if applicable) to clarify usage rights for this AI safety research project.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface 00quebec/Synthid-Bypass

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenCV

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenCV · recommended 1×
Pillow · recommended 1×
scikit-image · recommended 1×
TensorFlow · recommended 1×
Keras · recommended 1×

CATEGORY QUERY
How can I investigate techniques for removing invisible watermarks from AI-generated images?
you: not recommended
AI recommended (in order):
1. OpenCV
2. Pillow
3. scikit-image
4. TensorFlow
5. Keras
6. PyTorch
7. FastAI
8. FFmpeg
9. GIMP
AI recommended 9 alternatives but never named 00quebec/Synthid-Bypass. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Exploring methods to assess the resilience of digital watermarks in AI-generated content.
you: not recommended
AI recommended (in order):
1. CleverHans (tensorflow/cleverhans)
2. Foolbox (bethgelab/foolbox)
3. OpenCV (opencv/opencv)
4. scikit-image (scikit-image/scikit-image)
5. FFmpeg (FFmpeg/FFmpeg)
6. ImageHash (JohannesBuchner/imagehash)
7. phash
8. PyTorch (pytorch/pytorch)
9. TensorFlow (tensorflow/tensorflow)
AI recommended 9 alternatives but never named 00quebec/Synthid-Bypass. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
fail
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of 00quebec/Synthid-Bypass?
pass
AI did not name 00quebec/Synthid-Bypass — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts 00quebec/Synthid-Bypass in production, what risks or prerequisites should they evaluate first?
pass
AI named 00quebec/Synthid-Bypass explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo 00quebec/Synthid-Bypass solve, and who is the primary audience?
pass
AI named 00quebec/Synthid-Bypass explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of 00quebec/Synthid-Bypass. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/00quebec/Synthid-Bypass"><img src="https://repogeo.com/badge/00quebec/Synthid-Bypass.svg" alt="RepoGEO" /></a>

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00quebec/Synthid-Bypass — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite