RRepoGEO

REPOGEO REPORT · LITE

clpeng/Awesome-Face-Forgery-Generation-and-Detection

Default branch master · commit 2963bb2d · scanned 5/31/2026, 11:43:05 PM

GitHub: 780 stars · 112 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 clpeng/Awesome-Face-Forgery-Generation-and-Detection, 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
  • highreadme#1
    Clarify the README's opening to emphasize its role as a research resource, not a deployable tool

    Why:

    CURRENT
    A curated list of articles and codes related to face forgery generation and detection.
    COPY-PASTE FIX
    This repository serves as a comprehensive, curated awesome list of research articles, datasets, and code implementations related to face forgery generation and detection, primarily for researchers and developers in the field.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0) to clearly define usage terms.
  • mediumhomepage#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., the associated survey paper's official page, or a project page if one exists) to the repository's homepage field in the 'About' section.

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 clpeng/Awesome-Face-Forgery-Generation-and-Detection
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Sensity AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Sensity AI · recommended 1×
  2. Reality Defender · recommended 1×
  3. Truepic · recommended 1×
  4. OpenCV · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How can I detect manipulated images and videos in real-time applications?
    you: not recommended
    AI recommended (in order):
    1. Sensity AI
    2. Reality Defender
    3. Truepic
    4. OpenCV
    5. TensorFlow
    6. PyTorch
    7. Amped Authenticate
    8. FotoForensics
    9. Adobe Content Authenticity Initiative SDK
    10. imagehash

    AI recommended 10 alternatives but never named clpeng/Awesome-Face-Forgery-Generation-and-Detection. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available for generating realistic face swaps or manipulating facial expressions?
    you: not recommended
    AI recommended (in order):
    1. DeepFaceLab (DeepFaceLab/DeepFaceLab)
    2. FaceFusion (facefusion/facefusion)
    3. InsightFaceSwap (s0md3v/sd-webui-insightface)
    4. FaceApp
    5. Reface
    6. Swapface
    7. Adobe Photoshop

    AI recommended 7 alternatives but never named clpeng/Awesome-Face-Forgery-Generation-and-Detection. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 clpeng/Awesome-Face-Forgery-Generation-and-Detection?
    pass
    AI named clpeng/Awesome-Face-Forgery-Generation-and-Detection explicitly

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

  • If a team adopts clpeng/Awesome-Face-Forgery-Generation-and-Detection in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name clpeng/Awesome-Face-Forgery-Generation-and-Detection — 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?

  • In one sentence, what problem does the repo clpeng/Awesome-Face-Forgery-Generation-and-Detection solve, and who is the primary audience?
    pass
    AI did not name clpeng/Awesome-Face-Forgery-Generation-and-Detection — 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?

Embed your GEO score

Drop this badge into the README of clpeng/Awesome-Face-Forgery-Generation-and-Detection. 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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clpeng/Awesome-Face-Forgery-Generation-and-Detection — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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