EviChain: A scalable blockchain for accountable intelligent surveillance systems
Bibliographic record
Abstract
Smart cameras, as typical IoT devices, are widely adopted to provide surveillance on individuals, homes, and the environment. The unavoidably captured sensitive visuals via these cameras may raise significant security concerns, while the prevalent software defects and authentication misconfiguration issues aggravate the vulnerability of such devices. However, traditional cryptography techniques are inadequate to provide full protection of these devices due to the large computation overhead. In this context, realizing accountability for these surveillance systems shall be the last line of defense in the presence of fast-evolving and high-influential threats. We propose EviChain, a scalable blockchain-based solution to trace the operations on intelligent surveillance cameras and reserve the evidence for any misuse in tamper-proofing manipulation records. Building a blockchain over the distributed cameras is challenging due to the limited capacity of on-board memory. To tackle this challenge, we design a cooperative mechanism that enables cameras to adaptively join in groups and share storage for recording blocks. In addition, we present a computation efficiency and delay-aware block generation strategy to reduce the cost of the consensus process. We perform extensive simulations to validate the superior performance of EviChain over other baselines, for example, Practical Byzantine Fault Tolerance (PBFT).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".