Multi-Modal Content Identity: Unified Pipeline Across Image, Audio, Text, and Video

Mechanism

A content object is presented to the pipeline as a tuple of modality streams. For an image, the visual stream is the raster; the audio stream is empty; the structural stream is the file's container metadata, color profile, and any embedded provenance manifest. For audio, the audio stream is the waveform or its spectrogram; the visual stream is empty; the structural stream is the codec parameters and container metadata. For video, all three streams are populated, with the visual stream supplemented by a temporal-delta channel that captures inter-frame change. For text, the visual stream is empty, the audio stream is empty, and the structural stream is the token sequence with its formatting and document-structure metadata.

Each populated stream is passed to its modality-specific extractor, which produces an anchor: a fixed-length descriptor representing the stream's structural variance in a form that is stable under semantically irrelevant perturbations (re-encoding, mild compression, format conversion) but that diverges sharply under semantically meaningful edits (object insertion, voice splice, paragraph rewrite). The visual extractor produces a perceptual descriptor over the image or per-frame video; the audio extractor produces a spectro-temporal descriptor that captures pitch contour, formant structure, and rhythmic energy; the structural extractor produces a graph-theoretic descriptor over the document or container structure.

The anchors are then combined into a composite identity. The combination is not a simple concatenation; it is a structured commitment that records each anchor with its modality tag, its extractor version, and a coherence vector. The coherence vector measures the agreement among anchors that should describe the same underlying content. For a video, the visual and audio anchors must be temporally aligned and semantically consistent: the lip motion encoded in the visual anchor must agree with the phonetic content encoded in the audio anchor within a configurable tolerance. For a text-bearing image, the visual anchor's text region must agree with any structural anchor extracted from embedded OCR metadata.

Coherence is computed across pairs of modalities, not across all modalities at once. Each pair produces an agreement score; the composite identity records the full matrix of pairwise scores. A spoofed content object will exhibit pairs whose scores fall below a per-pair threshold; the identity remains valid only if every populated pair clears its threshold. The coherence test is therefore a structural conjunction: any single pair's failure invalidates the composite, regardless of how strongly the other pairs agree.

The composite identity is committed to an append-only lineage record. The record names the source object's bytewise hash, the modality streams populated, the anchor for each stream, the extractor versions, the pairwise coherence matrix, and the resulting validity verdict. Subsequent verifiers consume the record to re-derive any anchor independently and to confirm that the recorded coherence matrix is reproducible. The record is the artifact by which content provenance is established; it is not advisory metadata.

Cross-modal coherence is the anti-spoof primitive because spoofing a single modality is comparatively easy (a generative model can fabricate convincing audio or convincing video in isolation) but spoofing multiple modalities so that they agree at anchor level is substantially harder, particularly when the anchors are designed to capture cross-modal causal structure (lip-phoneme alignment, ambient-noise consistency, lighting-shadow coherence). The mechanism does not assert that cross-modal spoofing is impossible; it asserts that the cost of producing a coherent multi-modal forgery exceeds the cost of producing a single-modality forgery by a margin sufficient to deter casual fraud.

Operating Parameters

Anchor lengths are fixed at the extractor level. The reference visual extractor produces a 256-byte perceptual descriptor; the audio extractor produces a 384-byte spectro-temporal descriptor; the structural extractor produces a variable-length descriptor capped at 1024 bytes. Anchor length is a property of the extractor, not a parameter of the pipeline; substituting an extractor changes the length.

Coherence thresholds are expressed per modality pair as similarity scores in the range zero to one. The reference embodiment sets the visual-audio coherence threshold for video at 0.72, the visual-structural threshold for image-with-OCR at 0.85, the audio-structural threshold for narrated documents at 0.65, and the visual-structural threshold for video-with-manifest at 0.80. Thresholds below these values are permitted but flagged in the lineage record as low-confidence.

The temporal alignment tolerance for video coherence is 80 milliseconds by default, with a permitted range of 20 to 240 milliseconds. The narrower the tolerance, the stronger the spoofing barrier; the wider, the more robust to genuine production artifacts such as audio post-sync.

Extractor versions are committed to the lineage record. Verifiers presented with a record produced under an older extractor version may either re-extract under the current version (producing a fresh record bound to the original by hash) or accept the older record subject to a deprecation policy. The pipeline does not silently re-version anchors.

Empty streams are explicitly recorded as empty rather than being absent. A text document has empty visual and audio streams; the lineage record names them as empty. Coherence pairs involving an empty stream are not evaluated and are recorded as not-applicable rather than as passing.

Alternative Embodiments

In a first alternative embodiment, the visual extractor is a learned perceptual hash trained on a curated corpus of semantically equivalent transformations; in a second, it is an analytically derived structural hash based on wavelet energies; in a third, it is a hybrid that combines both, with the hybrid output being the longer of the two and the coherence test being run against both halves. The audio extractor admits analogous variants: learned, analytic, or hybrid.

The coherence function for visual-audio pairs may be embodied as a cross-correlation in a joint embedding space, as a phoneme-viseme alignment score, or as a generative-model likelihood that the audio could have produced the visual. The disclosure contemplates each variant; the pipeline is indifferent to the function so long as it is deterministic, monotonic in agreement, and bounded.

The composite identity may be embodied as a flat record of anchors and scores, as a Merkle tree over the anchors with the root committed to the lineage chain, or as a homomorphic commitment that permits selective disclosure of individual anchors without revealing the full composite. The selective-disclosure embodiment supports privacy-preserving verification scenarios where a verifier needs to confirm coherence without seeing the underlying content descriptors.

For streamed content, the pipeline may be embodied as an online extractor that produces anchors over windowed segments and commits a sequence of composite identities chained to a session root; this embodiment supports live-broadcast provenance without buffering the full stream.

Composition

Multi-modal content identity composes upward with provenance and rights-management layers: a content object whose composite identity is recorded in the lineage chain may be referenced by downstream attestations (authorship claims, usage licenses, redistribution rights), and any such attestation is bound to the specific anchor set under which it was issued. Modifying the content invalidates the composite identity, which in turn invalidates the dependent attestations without requiring their explicit revocation.

Downward, the pipeline composes with the codec and container layers of the underlying media. The structural extractor consumes container metadata and codec parameters as inputs; changes to these inputs are reflected in the structural anchor and may produce a coherence-test failure if the change affects content-bearing fields. The pipeline therefore detects container-level tampering as a side effect.

Laterally, the pipeline composes with content-addressed storage systems. The bytewise hash of the source object is recorded in the lineage entry alongside the composite identity; the hash and the identity are jointly addressable. Storage systems can use either as a key, and verifiers can confirm that a retrieved object hashes to the recorded value before invoking the coherence pipeline.

Prior-Art Separation

Perceptual hashing for images (pHash, dHash, and learned variants) is well established and produces single-modality descriptors. It does not bind multiple modalities and does not implement a coherence test. A perceptual hash of a video frame and a perceptual hash of the corresponding audio waveform exist as independent artifacts; nothing in the prior art ties them into a single identity whose validity depends on their agreement.

Audio fingerprinting systems (Shazam, AcoustID) similarly produce single-modality descriptors. They are designed for retrieval, not for provenance, and they do not implement cross-modal coherence.

Watermarking and steganographic provenance schemes embed a payload in the content itself. They are vulnerable to stripping (re-encoding the content removes the watermark) and to forgery (a generative model can produce content with a fabricated watermark). The disclosed mechanism does not embed any payload; it derives identity from the content's inherent structure across modalities.

Content provenance frameworks such as C2PA define manifest formats and signature schemes for binding metadata to content but rely on the manifest's continued attachment for verification. If the manifest is stripped, the binding is lost. The disclosed mechanism remains verifiable from the content alone, with the lineage record serving as a separate, infrastructure-level commitment rather than as content-attached metadata.

Deepfake-detection classifiers analyze single-modality artifacts (compression traces, frequency-domain anomalies) and produce a probabilistic verdict. They do not produce a stable identifier and do not bind across modalities; they are detectors, not anchors. The disclosed mechanism is complementary: a deepfake whose visual stream passes single-modality detection may still fail the coherence test because its fabricated visual stream does not align with the original audio.

Disclosure Scope

This article discloses the multi-modal pipeline architecture, the anchor extraction structure, the coherence-matrix composition, the operating parameters, and the alternative embodiments sufficient for a person of ordinary skill in media processing or content provenance to practice the invention. It does not disclose the specific weights of any learned extractor, which are treated as substitutable components; it does not disclose the lineage commitment cryptography, which is described in companion materials; and it does not disclose application-layer policies for deciding what action to take when coherence fails, which are application-engineering choices outside the scope of the patent. Claims arising from this disclosure are expected to read on the combination of multi-modal anchor extraction, structured pairwise coherence with per-pair thresholds, and lineage commitment of the composite identity, with dependent claims reaching the temporal-alignment tolerance, the streamed-content embodiment, and the selective-disclosure embodiment.

The disclosure further contemplates that the coherence matrix is a first-class artifact rather than an internal computation: the full pairwise score matrix is exposed to verifiers and is itself signed by the issuing pipeline. A verifier may therefore evaluate the matrix under a stricter local policy than the issuing policy, accepting the issued composite identity only if every pair clears a verifier-chosen threshold that may exceed the issuance threshold. This separation of issuance and acceptance policies permits high-stakes consumers to require stronger evidence than the default while still consuming records produced under the default. Claims reaching this aspect cover the matrix-signing embodiment, the verifier-side re-thresholding, and the recording of the verifier's chosen thresholds in a downstream lineage entry bound to the original.

The disclosure also contemplates that the extractor versioning carries an explicit forward-compatibility statement: when an extractor is revised, the revision manifest declares which classes of prior records remain comparable under the new version and which must be re-extracted to be jointly verifiable. This statement is committed alongside the extractor's identity and is consulted by verifiers presented with mixed-version evidence. The forward-compatibility statement is itself audit-grade and cannot be retroactively altered without breaking the chain that anchors the extractor's deployment history. Claims reaching this aspect cover the manifest format, its commitment to the chain, and its role in cross-version verification.

Finally, the disclosure contemplates that the empty-stream record is a load-bearing element of the audit, not a degenerate case: a content object claimed to be text-only must record empty visual and audio streams explicitly, and a verifier presented with such a record must reject any subsequent claim that the same object carries visual or audio content under the same identity. The explicit-empty discipline closes a class of attacks in which a forger appends modalities to an originally single-modality object and claims the original identity. Claims reaching this aspect cover the explicit-empty record, the rejection of post-hoc modality additions under the same composite identity, and the requirement that any modality addition produce a fresh composite identity bound to the prior by hash but distinct in the chain.