RRepoGEO

REPOGEO REPORT · LITE

zhipeixu/FakeShield

Default branch main · commit b22717d7 · scanned 6/9/2026, 2:17:49 AM

GitHub: 607 stars · 36 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 zhipeixu/FakeShield, 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

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

OVERALL DIRECTION
  • hightopics#1
    Expand repository topics for better categorization

    Why:

    CURRENT
    ifdl, mllm
    COPY-PASTE FIX
    deepfake-detection, image-forgery-detection, ai-forensics, multi-modal-llm, deep-learning, computer-vision, research
  • highreadme#2
    Collapse the 'other projects' section in the README by default

    Why:

    CURRENT
    <details open><summary>💡 We also have other Copyright Protection projects that may interest you ✨. </summary><p>
    COPY-PASTE FIX
    <details><summary>💡 We also have other Copyright Protection projects that may interest you ✨. </summary><p>
  • mediumreadme#3
    Add a concise introductory sentence to the README

    Why:

    COPY-PASTE FIX
    FakeShield is a cutting-edge research project for explainable image forgery detection and localization, specifically targeting AI-generated images from diffusion models using multi-modal large language models.

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 zhipeixu/FakeShield
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FotoForensics
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. FotoForensics · recommended 1×
  2. Forensically · recommended 1×
  3. GIMP (GNU Image Manipulation Program) · recommended 1×
  4. Amped Authenticate · recommended 1×
  5. OpenCV (Open Source Computer Vision Library) · recommended 1×
  • CATEGORY QUERY
    How can I detect manipulated images and understand the specific alterations made?
    you: not recommended
    AI recommended (in order):
    1. FotoForensics
    2. Forensically
    3. GIMP (GNU Image Manipulation Program)
    4. Amped Authenticate
    5. OpenCV (Open Source Computer Vision Library)
    6. Python
    7. Pillow
    8. exiftool
    9. ExifTool
    10. Google's DeepFake Detection Dataset
    11. Facebook's DeepFake Detection Challenge models
    12. TensorFlow
    13. PyTorch

    AI recommended 13 alternatives but never named zhipeixu/FakeShield. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What AI tools are available for identifying deepfake images and localizing forged regions?
    you: not recommended
    AI recommended (in order):
    1. DeepFake Detection Challenge (DFDC) Dataset and Models
    2. FaceForensics++ (FF++) Dataset and Associated Models
    3. DeepFake-o-meter (DFOM)
    4. ForensicTrails
    5. Sensity AI
    6. Spatio-Temporal Attention Network (STAN) based models
    7. XceptionNet

    AI recommended 7 alternatives but never named zhipeixu/FakeShield. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 zhipeixu/FakeShield?
    pass
    AI named zhipeixu/FakeShield explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts zhipeixu/FakeShield in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zhipeixu/FakeShield 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 zhipeixu/FakeShield solve, and who is the primary audience?
    pass
    AI named zhipeixu/FakeShield 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 zhipeixu/FakeShield. 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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zhipeixu/FakeShield — 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