ScienceToStartup
TrendsTopicsSavedArticlesChangelogCareersAbout

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs
All systems operational

Product

  • Dashboard
  • GitHub Velocity
  • Workspace
  • Build Loop
  • Research Map
  • Trends
  • Topics
  • Articles

Enterprise

  • TTO Dashboard
  • Scout Reports
  • RFP Marketplace
  • API

Resources

  • All Resources
  • Benchmark
  • Database
  • Dataset
  • Calculator
  • Glossary
  • State Reports
  • Industry Index
  • Directory
  • Templates
  • Alternatives
  • Changelog
  • FAQ
  • Docs

Company

  • About
  • Careers
  • For Media
  • Privacy Policy
  • Legal
  • Contact

Community

  • Open Source
  • Community
ScienceToStartup

Copyright © 2026 ScienceToStartup. All rights reserved.

Privacy Policy|Legal
  1. Home
  2. Signal Canvas
  3. Backdoor Directions in Vision Transformers
← Back to Paper

Backdoor Directions in Vision Transformers

Fresh4d ago
Export BriefOpen in Build LoopConnect with Author
View PDF ↗
Viability
0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Backdoor Directions in Vision Transformers

PDF: https://arxiv.org/pdf/2603.10806v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

Backdoor Directions in Vision Transformers

Overall score: 4/10
Lineage: 5b7c206017d7…
Cmd/Ctrl+K
Search the latest paper corpus with startup-focused AI synthesis.

Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

Missingness
  • - repo_url
  • - references
  • - proof_status
  • - distribution_readiness_scores
  • - paper_extraction_scorecards
Unknowns
  • - distribution readiness has not been computed yet
  • - proof verification has not been recorded yet

Mode Notes

  • Corpus mode searches the research corpus broadly.
  • Paper mode pins trust state to the canonical paper kernel.
  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

Starting…

Dimensions overall score 4.0

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Claim extraction is still pending for this paper. Check back after the next analysis run.

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Keep exploring

Builds On This
Removing the Trigger, Not the Backdoor: Alternative Triggers and Latent Backdoors
Score 2.0down
Builds On This
Detecting and Eliminating Neural Network Backdoors Through Active Paths with Application to Intrusion Detection
Score 3.0down
Builds On This
What Helps -- and What Hurts: Bidirectional Explanations for Vision Transformers
Score 3.0down
Builds On This
The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers
Score 2.0down
Higher Viability
BackdoorIDS: Zero-shot Backdoor Detection for Pretrained Vision Encoder
Score 8.0up
Higher Viability
STRAP-ViT: Segregated Tokens with Randomized -- Transformations for Defense against Adversarial Patches in ViTs
Score 7.0up
Higher Viability
Test-Time Attention Purification for Backdoored Large Vision Language Models
Score 7.0up
Higher Viability
Delayed Backdoor Attacks: Exploring the Temporal Dimension as a New Attack Surface in Pre-Trained Models
Score 5.0up

Startup potential card

Startup potential card preview
Share on XLinkedIn

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Recommended Stack

PyTorchML Framework
OpenCVComputer Vision
Ultralytics YOLOComputer Vision
Stability AIGenerative AI
RoboflowComputer Vision

Startup Essentials

Banana.dev

GPU Inference

Hugging Face Hub

ML Model Hub

Modal

Serverless GPU

Replicate

Run ML Models

Render

Deploy Backend

Railway

Full-Stack Deploy

Supabase

Backend & Auth

Vercel

Deploy Frontend

Estimated $10K - $14K over 6-10 weeks.

MVP Investment

$10K - $14K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
SaaS Stack
$800
Domain & Legal
$500

6mo ROI

0.5-1.5x

3yr ROI

5-12x

Computer vision products require more validation time. Hardware integrations may slow early revenue, but $100K+ deals at 3yr are common.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Talent Scout

Find Builders

Computer experts on LinkedIn & GitHub

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.