Witnessd: Proof-of-process via Adversarial Collapse

• URL: http://arxiv.org/abs/2602.01663v1
• Date: Mon, 02 Feb 2026 05:30:21 GMT
• Title: Witnessd: Proof-of-process via Adversarial Collapse
• Authors: David Condrey,
• Abstract summary: We address the gap between cryptographic integrity and process provenance.<n>We introduce proof-of-process, a primitive category for evidence that a physical process, not merely a signing key, produced a digital artifact.<n>We present Witnessd, an architecture combining jitter seals with Verifiable Delay Functions, external timestamp anchors, dual-source keystroke validation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital signatures prove key possession, not authorship. An author who generates text with AI, constructs intermediate document states post-hoc, and signs each hash produces a signature chain indistinguishable from genuine composition. We address this gap between cryptographic integrity and process provenance. We introduce proof-of-process, a primitive category for evidence that a physical process, not merely a signing key, produced a digital artifact. Our construction, the jitter seal, injects imperceptible microsecond delays derived via HMAC from a session secret, keystroke ordinal, and cumulative document hash. Valid evidence requires that real keystrokes produced the document through those intermediate states. We propose the Adversarial Collapse Principle as an evaluation criterion: evidence systems should be judged by whether disputing them requires a conjunction of specific, testable allegations against components with independent trust assumptions. We present Witnessd, an architecture combining jitter seals with Verifiable Delay Functions, external timestamp anchors, dual-source keystroke validation, and optional hardware attestation. Each layer forces allegations at different capability levels; disputing authentic evidence requires coordinated claims across independent trust boundaries. The system does not prevent forgery: a kernel-level adversary can defeat it, and typing AI-generated content produces valid evidence. The contribution is converting vague doubt into falsifiable allegations. We evaluate across 31,000 verification trials with deterministic rejection of invalid proofs.

Related papers

• Authenticated Contradictions from Desynchronized Provenance and Watermarking [48.47756819432157]
  This work formalizes and empirically demonstrates the $textitIntegrity Clash$, a condition in which a digital asset carries a cryptographically valid C2PA manifest asserting human authorship.<n>We propose a cross-layer audit protocol that jointly evaluates provenance metadata and watermark detection status, achieving 100% classification accuracy across 3,500 test images.
  arXiv  Detail & Related papers  (2026-03-02T20:42:12Z)
• Privacy-Preserving Proof of Human Authorship via Zero-Knowledge Process Attestation [0.0]
  ZK-PoP is a construction that allows a verifier to confirm that (a) sequential work function chains were computed correctly, (b) behavioral feature vectors fall within human population, and (c) content evolution is consistent with incremental human editing.<n>We prove that ZK-PoP is computationally zero-knowledge, computationally sound, and unlinkability across sessions.
  arXiv  Detail & Related papers  (2026-02-26T20:38:19Z)
• Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification [0.0]
  The proliferation of AI-generated text has intensified the need for reliable authorship verification, yet current output-based methods are increasingly unreliable.<n>We observe that the ordinary typing interface captures rich cognitive signatures, measurable patterns in keystroke timing that reflect the planning, translating, and revising stages of genuine composition.<n>We present a non-intrusive verification framework that operates within existing writing interfaces, collecting only timing metadata to preserve privacy.
  arXiv  Detail & Related papers  (2026-02-26T20:02:55Z)
• MerkleSpeech: Public-Key Verifiable, Chunk-Localised Speech Provenance via Perceptual Fingerprints and Merkle Commitments [0.0]
  We propose MerkleSpeech, a system for public-key verifiable, chunk-localised speech provenance.<n>The system computes perceptual fingerprints over short speech chunks, commits them in a Merkle tree whose root is signed with an issuer key.<n>We present experiments targeting very low false positive rates under resampling, bandpass filtering, and additive noise.
  arXiv  Detail & Related papers  (2026-02-10T11:58:19Z)
• Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities [32.76303717104482]
  We introduce DeR2, a controlled deep-research sandbox that isolates document-grounded reasoning.<n>DeR2 decouples evidence access from reasoning via four regimes--Instruction-only, Concepts, Related-only, and Full-set.<n> Experiments across a diverse set of state-of-the-art foundation models reveal substantial variation and significant headroom.
  arXiv  Detail & Related papers  (2026-01-29T16:26:19Z)
• Proofs of No Intrusion [9.813075714350743]
  We introduce Proofs of No Intrusion, which enable a classical client to remotely test whether a quantum server has been hacked.<n>The test does not destroy the data being tested, avoiding the need to store a backup elsewhere.<n>We show how to equip several constructions of unclonable primitives with proofs of non-intrusion.
  arXiv  Detail & Related papers  (2025-10-07T20:11:20Z)
• Contrastive Learning to Improve Retrieval for Real-world Fact Checking [84.57583869042791]
  We present Contrastive Fact-Checking Reranker (CFR), an improved retriever for fact-checking complex claims. We leverage the AVeriTeC dataset, which annotates subquestions for claims with human written answers from evidence documents. We find a 6% improvement in veracity classification accuracy on the dataset.
  arXiv  Detail & Related papers  (2024-10-07T00:09:50Z)
• Evidence Tampering and Chain of Custody in Layered Attestations [0.0]
  In distributed systems, trust decisions are made on the basis of integrity evidence generated via remote attestation. We present algorithms for identifying all such tampering opportunities for given evidence as well as tampering "strategies" by which an adversary can modify incriminating evidence without being detected. Our efforts are intended to help protocol designers ensure their protocols reduce evidence tampering opportunities to the smallest, most trustworthy set of components possible.
  arXiv  Detail & Related papers  (2024-01-31T21:54:53Z)
• Complex Claim Verification with Evidence Retrieved in the Wild [73.19998942259073]
  We present the first fully automated pipeline to check real-world claims by retrieving raw evidence from the web. Our pipeline includes five components: claim decomposition, raw document retrieval, fine-grained evidence retrieval, claim-focused summarization, and veracity judgment.
  arXiv  Detail & Related papers  (2023-05-19T17:49:19Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)
• Quantum Proofs of Deletion for Learning with Errors [91.3755431537592]
  We construct the first fully homomorphic encryption scheme with certified deletion. Our main technical ingredient is an interactive protocol by which a quantum prover can convince a classical verifier that a sample from the Learning with Errors distribution in the form of a quantum state was deleted.
  arXiv  Detail & Related papers  (2022-03-03T10:07:32Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)
• Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification [5.836068916903788]
  Hierarchical Evidence Set Modeling (HESM) is a framework to extract evidence sets and verify a claim to be supported, refuted or not enough info. Our experimental results show that HESM outperforms 7 state-of-the-art methods for fact extraction and claim verification.
  arXiv  Detail & Related papers  (2020-10-10T22:27:17Z)
• Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder [104.25716317141321]
  We propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach provides state-of-the-art performance on both Event2Mind and ATOMIC datasets.
  arXiv  Detail & Related papers  (2020-06-15T02:59:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.