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ARXIV:2604.00605 · ADVERSARIAL ROBUSTNESS · SUBMITTED 02 APR · 20:56 UTC · FRESHNESS STALE
ARXIV:2604.00605ADVERSARIAL ROBUSTNESSSUBMITTED 02 APR · 20:56 UTCFRESHNESS STALEDaye Kang · Hyeongboo Baek · arXiv
This research identifies a new adversarial failure mode in spiking neural network object detectors where detection count is preserved but accuracy collapses.
Opportunity summary
Pain This research identifies a new adversarial failure mode in spiking neural network object detectors where detection count is preserved but accuracy collapses.
Evidence 40 refs | 3 sources | 33% coverage
Blocker Evidence unverified
This research identifies a new adversarial failure mode in spiking neural network object detectors where detection count is preserved but accuracy collapses. This coupling was assumed, not measured.
The primary tools used to monitor and defend object detectors under adversarial attack assume that when accuracy degrades, detection count drops in tandem. This coupling was assumed, not measured.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These results provide, to our knowledge, the first evidence that adversarial failure modes can be substrate-dependent.
Adversarial Robustness moved forward this cycle; last verified April 2026. Public score 3.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score3.0Analysis summary
This research identifies a new adversarial failure mode in spiking neural network object detectors where detection count is preserved but accuracy collapses.
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Paper Pack
10.48550/arXiv.2604.00605This research identifies a new adversarial failure mode in spiking neural network object detectors where detection count is preserved but accuracy collapses.
Abstract
The primary tools used to monitor and defend object detectors under adversarial attack assume that when accuracy degrades, detection count drops in tandem. This coupling was assumed, not measured. We report a counterexample observed on a single model: under standard PGD, EMS-YOLO, a spiking neural network (SNN) object detector, retains more than 70% of its detections while mAP collapses from 0.528 to 0.042. We term this count-preserving accuracy collapse Quality Corruption (QC), to distinguish it from the suppression that dominates untargeted evaluation. Across four SNN architectures and two threat models (l-infinity and l-2), QC appears only in one of the four detectors tested (EMS-YOLO). On this model, all five standard defense components fail to detect or mitigate QC, suggesting the defense ecosystem may rely on a shared assumption calibrated on a single substrate. These results provide, to our knowledge, the first evidence that adversarial failure modes can be substrate-dependent.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
unverified40 refs; 3 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
This research identifies a new adversarial failure mode in spiking neural network object detectors where detection count is preserved but accuracy collapses. This coupling was assumed, not measured.
METHOD
The primary tools used to monitor and defend object detectors under adversarial attack assume that when accuracy degrades, detection count drops in tandem. This coupling was assumed, not measured.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These results provide, to our knowledge, the first evidence that adversarial failure modes can be substrate-dependent.
WHY NOW
Adversarial Robustness moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This research identifies a new adversarial failure mode in spiking neural network object detectors where detection count is preserved but accuracy collapses. This coupling was assumed, not measured.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The primary tools used to monitor and defend object detectors under adversarial attack assume that when accuracy degrades, detection count drops in tandem. This coupling was assumed, not measured.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These results provide, to our knowledge, the first evidence that adversarial failure modes can be substrate-dependent.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Adversarial Robustness moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
This research identifies a new adversarial failure mode in spiking neural network object detectors where detection count is preserved but accuracy collapses.
Segment
Adversarial Robustness
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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Hacker News
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Bluesky
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CITED BY
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Foundation
Commercially relevant
Conflicting
Owned Distribution
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
40 refs / 3 sources / 33% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
40 references, 3 sources, 33% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.