{"id":"50cdbee8-407a-4be9-9beb-edcb3266a085","arxiv_id":"2604.08104","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"Quantum Vision theory transforms speech spectrograms and MFCCs into information waves, enabling QV-CNN and QV-ViT models to reach up to 94.57% accuracy on the ASVspoof deepfake detection dataset.","lead":"This paper proposes Quantum Vision theory, which converts audio features like spectrograms into information waves inspired by quantum particle-wave duality before classification with CNNs and Vision Transformers for deepfake speech detection. A smart generalist might read it to see whether quantum-inspired data representations can meaningfully boost performance in audio security tasks.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Gains may not be due to the QV information-wave transform; baselines may differ in architecture, hyperparameters, or preprocessing.","rationale":"The reader's weakest assumption is precisely the load-bearing point: without isolating the QV block, the numerical results cannot support the claim that 'QV theory is an effective and promising approach.' The abstract-only limitation noted by the reader makes the concern even more acute; the full text would need to contain both the block definition and matched ablations for the claim to be secure. This is an internal consistency issue rather than an external-consensus disagreement.","tokens_in":1861,"tokens_out":462,"duration_ms":19319,"concrete_test":"From the methods section, extract the exact mathematical definition of the QV block (including any learnable parameters or fixed transforms). Re-implement the reported QV-CNN architecture but replace the QV block with an identity or standard per-feature normalization; train both versions on the identical ASVspoof splits, optimizer, and hyper-parameters used for the published QV-CNN run. If the non-QV control reaches within 1% accuracy / 1% EER of the published numbers, the headline improvement is not attributable to the wave transformation.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim requires that QV-CNN/QV-ViT outperform standard CNN/ViT specifically because inputs are first mapped to 'information waves' via the QV block (inspired by particle-wave duality). For this attribution to be valid, the only systematic difference between the QV and non-QV pipelines must be that block; all other factors (network depth/width, optimizer, learning-rate schedule, data augmentation, feature extraction parameters, and training epochs) must be identical. The abstract reports 94.57% accuracy for QV-CNN on Mel-spectrograms and 94.20%/9.04% EER for MFCC, but supplies no equation or pseudocode for the QV block itself and no ablation that disables the wave transform while freezing everything else. If the block introduces extra parameters, non-linearities, or normalization steps not present in the reported baselines, the performance delta cannot be credited to QV theory.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes Quantum Vision (QV) theory, inspired by particle-wave duality, as a new approach for deep learning-based audio classification in deepfake speech detection. Speech features (STFT, Mel-spectrograms, MFCC) are transformed into 'information waves' via a QV block and fed to QV-CNN and QV-ViT models. On the ASVspoof dataset, the authors claim QV-based models consistently outperform standard CNN and ViT, with QV-CNN on Mel-spectrograms reaching 94.57% accuracy and QV-CNN on MFCC achieving 94.20% accuracy and 9.04% EER.","tokens_in":2087,"tokens_out":536,"duration_ms":27236,"significance":"If the reported gains are shown to arise specifically from the information-wave transformation rather than architectural or hyperparameter differences, the work could establish a novel quantum-inspired paradigm for audio perception tasks and improve robustness in deepfake detection. The approach extends prior QV ideas from images to spectrograms, but its significance hinges on verifiable attribution and reproducibility.","major_comments":[{"comment":"Abstract: The QV block is described only at a high level with no equations, pseudocode, or specification of internal parameters/normalization for creating information waves. This is load-bearing for the central claim, as the performance deltas (e.g., 94.57% accuracy) cannot be attributed to QV theory without knowing whether the block introduces extra parameters, non-linearities, or data handling absent from baselines.","section":"Abstract"},{"comment":"Abstract / implied Methods and Results: No details on experimental protocol, matched baselines (identical CNN/ViT depth, optimizer, learning rate, data augmentation, feature extraction parameters, and epochs between QV and non-QV), statistical significance tests, error bars, or ablation studies (e.g., disabling the wave transform while freezing all else). Without these, the claim that QV-CNN/QV-ViT outperform specifically due to the QV block cannot be evaluated.","section":"Abstract"}],"minor_comments":[{"comment":"Abstract: Consider specifying dataset splits, number of samples, and exact baseline model configurations to allow direct comparison.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"The manuscript introduces an interesting concept but the provided text lacks the technical specificity needed for a cs.CL venue; check full paper for related work on quantum-inspired methods and ensure no overclaiming of novelty."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comments, which highlight important areas for improving clarity and rigor in our presentation of Quantum Vision theory for audio deepfake detection. We address each major comment below and commit to revisions that will strengthen the manuscript without altering its core claims.","responses":[{"response":"We agree that the abstract provides only a high-level overview of the QV block. The full manuscript expands on the particle-wave duality inspiration and the transformation of spectrograms/MFCCs into information waves, but we acknowledge that explicit equations, pseudocode, and parameter details are needed for full attribution of results. In revision, we will add these elements to both the abstract and a new dedicated subsection in Methods, including the mathematical definition of the wave transform, implementation steps, and any normalization or parameters used. This will confirm that the QV block's contribution is isolated from baseline differences.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The QV block is described only at a high level with no equations, pseudocode, or specification of internal parameters/normalization for creating information waves. This is load-bearing for the central claim, as the performance deltas (e.g., 94.57% accuracy) cannot be attributed to QV theory without knowing whether the block introduces extra parameters, non-linearities, or data handling absent from baselines."},{"response":"We recognize that detailed experimental protocols are essential to substantiate that gains arise from the QV transformation. The manuscript reports consistent outperformance on ASVspoof with specific accuracy and EER figures for QV-CNN and QV-ViT versus standard models, but we will revise the Methods and Results sections to include full matched baseline specifications (identical architectures, hyperparameters, augmentation, and training settings), the complete experimental protocol, statistical significance tests, error bars from multiple runs, and ablation studies that isolate the wave transform by disabling it while freezing all other components. These additions will enable direct evaluation of the QV block's role.","revision_made":"yes","referee_comment":"[Abstract] Abstract / implied Methods and Results: No details on experimental protocol, matched baselines (identical CNN/ViT depth, optimizer, learning rate, data augmentation, feature extraction parameters, and epochs between QV and non-QV), statistical significance tests, error bars, or ablation studies (e.g., disabling the wave transform while freezing all else). Without these, the claim that QV-CNN/QV-ViT outperform specifically due to the QV block cannot be evaluated."}],"tokens_in":1590,"tokens_out":545,"duration_ms":68173,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"This paper takes the quantum vision idea from image work and applies it to audio for deepfake speech detection. They convert STFT, Mel-spectrograms, and MFCC features into information waves with a QV block, then train QV-CNN and QV-ViT models on the ASVspoof dataset. The QV versions beat the standard CNN and ViT baselines, with the best numbers at 94.57% accuracy for QV-CNN on Mel-spectrograms and 94.20% accuracy with 9.04% EER for QV-CNN on MFCC features.","headline":"The paper extends a quantum vision block to audio spectrograms for deepfake detection and reports accuracy gains, but missing ablations and block details make it hard to credit the quantum part.","tokens_in":2583,"tokens_out":207,"would_cite":false,"duration_ms":23568,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Converting speech features into information waves improves deepfake detection accuracy.","keywords":["quantum vision theory","deepfake detection","audio classification","information waves","QV-CNN","MFCC","Mel-spectrogram","ASVspoof"],"falsifier":"Retraining an identical QV-CNN architecture on the same ASVspoof split but bypassing the QV block and still obtaining 94 percent or higher accuracy would falsify the claim that the wave step drives the improvement.","tokens_in":2770,"feed_emoji":"🌊","tokens_out":707,"duration_ms":48733,"temperature":0.7,"pith_summary":"The paper proposes Quantum Vision theory for audio tasks by first transforming speech features such as STFT, Mel-spectrograms, and MFCC into information waves through a dedicated QV block. These wave representations are then passed to modified convolutional networks and vision transformers for classifying genuine versus spoofed speech on the ASVspoof dataset. The QV-enhanced models consistently reach higher accuracy and lower error rates than standard CNN and ViT baselines trained on the same untransformed features. Peak results include 94.57 percent accuracy with Mel-spectrograms and 94.20 percent accuracy with 9.04 percent EER using MFCC under the QV-CNN architecture.","feed_headline":"QV wave transform achieves 94.57% accuracy for deepfake speech detection","feed_subtitle":"Converting MFCC and Mel-spectrograms into information waves lets QV-CNN and QV-ViT outperform standard models on ASVspoof.","key_machinery":"The QV block, which converts input audio features into information waves inspired by particle-wave duality before they enter the neural network layers.","core_discovery":"QV theory treats data as having both a collapsed observable form and an information-wave form. By routing audio features through a QV block that performs this wave transformation before classification, the resulting QV-CNN and QV-ViT models achieve higher accuracy and better robustness than their non-QV counterparts when distinguishing real from deepfake speech on ASVspoof, with the strongest single result being 94.57 percent accuracy.","pith_inferences":["The same preprocessing step could be applied to other audio tasks such as speaker verification or environmental sound classification to test whether wave forms add useful structure.","If the gains hold on larger or noisier datasets, the approach may point to a general benefit of preserving phase-like information that standard spectrogram pipelines discard.","Extending the block to video or multimodal inputs would reveal whether the duality principle transfers beyond audio."],"forward_implications":["QV-CNN using MFCC features reaches 94.20 percent accuracy and 9.04 percent EER.","QV-CNN using Mel-spectrograms reaches 94.57 percent accuracy.","Both QV-CNN and QV-ViT outperform standard CNN and ViT models in accuracy and robustness on genuine-versus-spoofed classification.","The wave representation improves separation of real and deepfake speech signals across multiple feature types."],"fun_headline_variants":["QV block converts audio spectrograms into information waves","Wave transformed MFCC features yield 94.57 percent accuracy","Quantum vision applied to speech for deepfake classification","Information waves enable better QV CNN results on ASVspoof"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The measured accuracy gains arise specifically from the information-wave transformation rather than from any unstated differences in architecture, hyperparameters, or data handling.","fun_headline_variants_meta":{"raw":{"variants":["QV block converts audio spectrograms into information waves","Wave transformed MFCC features yield 94.57 percent accuracy","Quantum vision applied to speech for deepfake classification","Information waves enable better QV CNN results on ASVspoof"]},"model":"grok-4.3","cost_usd":0.008356,"raw_usage":{"total_tokens":3859,"prompt_tokens":818,"num_sources_used":0,"completion_tokens":63,"cost_in_usd_ticks":83562000,"prompt_tokens_details":{"text_tokens":818,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2978,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":818,"tokens_out":63,"duration_ms":34539,"temperature":1.0,"reasoning_tokens":2978,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-10T17:39:07.353735+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Retraining an identical QV-CNN architecture on the same ASVspoof split but bypassing the QV block and still obtaining 94 percent or higher accuracy would falsify the claim that the wave step drives the improvement.","supporting_citations":[],"review_version":1}