Version 1.0 - May 2025
HyperDeteX represents a major breakthrough in the fight against voice deepfakes, combining artificial intelligence and blockchain technology to create a decentralized ecosystem for synthetic voice detection. Our platform rewards users who contribute to training AI models, creating a virtuous cycle of continuous improvement in detection capabilities.
To protect the authenticity of voice communication in the digital age by developing accessible and effective detection solutions, supported by an engaged community.
To become the global standard for synthetic voice detection, establishing a trust framework for digital voice communications.
In an era where artificial intelligence has made the creation of synthetic voices increasingly sophisticated and accessible, the need for reliable detection mechanisms has become paramount. HyperDeteX emerges as a pioneering solution at the intersection of AI, blockchain technology, and community-driven development.
Our platform leverages the power of decentralized networks and machine learning to create a robust ecosystem where contributors are incentivized to participate in the development and improvement of voice detection systems. This approach ensures continuous evolution and adaptation to new synthetic voice generation techniques.
The proliferation of synthetic voice technology presents significant challenges across multiple sectors. From financial fraud to social engineering, the ability to create convincing voice deepfakes has opened new vectors for malicious activities. Traditional detection methods are struggling to keep pace with rapidly evolving generation techniques.
$5B+
Annual losses from voice fraud
250%
Increase in deepfake incidents
85%
Companies seeking solutions
HyperDeteX employs a hybrid architecture combining edge computing for real-time detection with blockchain technology for secure verification and reward distribution. Our solution integrates advanced AI models with decentralized storage and processing capabilities.
Detection Speed
<100ms
Average response time
Accuracy Rate
99.9%
Detection precision
Processing Power
1M+
Samples per second
HyperDeteX employs a hybrid multi-modal deep neural network architecture specifically designed for real-time synthetic voice detection. Our model combines spectral, temporal, and linguistic features through a sophisticated ensemble approach, achieving state-of-the-art performance with sub-millisecond inference times.
Primary Path:
• Spectral Feature Extractor (CNN)
• Temporal Sequence Analyzer (BiLSTM)
• Attention Mechanism Layer
Auxiliary Path:
• Raw Waveform Processor (1D-CNN)
• Prosodic Feature Extractor
• Cross-Modal Fusion Layer
Given a raw audio signal x(t) sampled at 16kHz, we first apply Short-Time Fourier Transform (STFT):
X(m,k) = Σn=-∞∞ x(n) · w(n-mH) · e-j2πkn/N
where m is the frame index, k is the frequency bin, H is the hop size, and w(n) is the Hann window
We extract Mel-frequency cepstral coefficients (MFCCs) and their derivatives:
M(m) = DCT{log(Mel{|X(m,k)|²})}
ΔM(m) = M(m+1) - M(m-1)
ΔΔM(m) = ΔM(m+1) - ΔM(m-1)
Feature vector: F(m) = [M(m), ΔM(m), ΔΔM(m)] ∈ ℝ39
The convolutional layers learn hierarchical representations:
hl(i,j) = σ(ΣmΣn Wl(m,n) · hl-1(i+m,j+n) + bl)
with ReLU activation: σ(x) = max(0, x)
where l indexes the layer, (i,j) the spatial position, and Wl the learnable filters
Temporal dependencies are captured using BiLSTM cells:
ft = σ(Wf · [ht-1, xt] + bf)
it = σ(Wi · [ht-1, xt] + bi)
C̃t = tanh(WC · [ht-1, xt] + bC)
Ct = ft * Ct-1 + it * C̃t
ht = ot * tanh(Ct)
Final output: hBiLSTM = [h⃗t, h⃖t] (concatenated forward and backward states)
Multi-head self-attention for important feature highlighting:
Attention(Q,K,V) = softmax(QKT/√dk)V
MultiHead(Q,K,V) = Concat(head1,...,headh)WO
where headi = Attention(QWiQ, KWiK, VWiV)
h = 8 attention heads, dk = 64 dimensions per head
Binary classification with confidence estimation:
z = Wout · hfinal + bout
P(synthetic|x) = σ(z) = 1/(1 + e-z)
Confidence = max(P(synthetic|x), 1 - P(synthetic|x))
Loss function: L = -Σ[y log ŷ + (1-y) log(1-ŷ)] + λ||W||₂²
HyperDeteX Neural Network Architecture
Raw Audio Signal (16kHz, 3s segments)
↓
┌─────────────────────────────────────┐
│ STFT + MFCC Preprocessing │ → Feature Maps (39 × 187)
│ • Window: Hann (25ms, 10ms hop) │
│ • FFT size: 512, Mel filters: 39 │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐ ┌─────────────────────────────────────┐
│ CNN Block 1 │ │ 1D-CNN Path │
│ Conv2D: 64@3×3, stride=1, pad=1 │ │ Conv1D: 32@15, stride=2, pad=7 │
│ BatchNorm2D + ReLU │ │ BatchNorm1D + ReLU │
│ MaxPool2D: 2×2, stride=2 │ │ Conv1D: 64@9, stride=2, pad=4 │
└─────────────────────────────────────┘ │ BatchNorm1D + ReLU │
↓ │ Conv1D: 128@5, stride=2, pad=2 │
┌─────────────────────────────────────┐ │ BatchNorm1D + ReLU │
│ CNN Block 2 │ └─────────────────────────────────────┘
│ Conv2D: 128@3×3, stride=1, pad=1 │ ↓
│ BatchNorm2D + ReLU │ ┌─────────────────────────────────────┐
│ MaxPool2D: 2×2, stride=2 │ │ Global AvgPool1D │
│ Dropout2D: p=0.25 │ │ + Dropout: p=0.2 │
└─────────────────────────────────────┘ │ → Features (128) │
↓ └─────────────────────────────────────┘
┌─────────────────────────────────────┐ ↓
│ CNN Block 3 │ │
│ Conv2D: 256@3×3, stride=1, pad=1 │ │
│ BatchNorm2D + ReLU │ │
│ MaxPool2D: 2×2, stride=2 │ │
│ Dropout2D: p=0.3 │ │
│ → Features (384) │ │
└─────────────────────────────────────┘ │
↓ │
└──────────────────┬────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Feature Fusion │ → Combined (512)
│ Linear: 512 → 512 │
│ LayerNorm + ReLU + Dropout(0.1) │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ BiLSTM Layers │ → Temporal (256)
│ LSTM: hidden=128, layers=2 │
│ Bidirectional, dropout=0.2 │
│ Output: [forward, backward] │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Multi-Head Attention │ → Attended (256)
│ heads=8, d_model=256, d_k=32 │
│ dropout=0.1, pos_encoding=True │
│ LayerNorm + residual connections │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Dense Layers │ → Classification
│ Linear: 256 → 128 │
│ BatchNorm1D + ReLU + Dropout(0.3) │
│ Linear: 128 → 64 │
│ BatchNorm1D + ReLU + Dropout(0.2) │
│ Linear: 64 → 1 │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Output Layer │ → P(synthetic)
│ Sigmoid activation │
│ + Confidence estimation │
│ Temperature scaling: τ=1.2 │
└─────────────────────────────────────┘
Training set: 2.4M samples
Learning Rate: 1e-4
Batch Size: 64
Optimizer: AdamW
Weight Decay: 1e-5
Epochs: 100
LR Schedule: Cosine
Warmup: 10 epochs
Early Stop: 15
Audio Augmentations
Environmental
Adversarial
Current dataset:
Target for Q4 2025:
| Pred: Real | Pred: Synth | |
|---|---|---|
| True: Real | 12,458 | 5 |
| True: Synth | 11 | 12,526 |
Test set: 25,000 samples
Inference Time
47ms
Average (GPU)
Model Size
23.4MB
Compressed
Parameters
4.7M
Trainable
FLOPS
2.1G
Per sample
Loss
0.8 │
│
0.6 │\
│ \
0.4 │ \___
│ \___
0.2 │ \______
│ \____
0.0 │________________________\____
0 20 40 60 80 100
Epochs
Training Loss: █
Validation Loss: ▓
Acc(%)
100 │ ████████
│ █████
95 │ █████
│ █████
90 │█████
│
85 │
│
80 │
0 20 40 60 80 100
Epochs
Training Acc: █
Validation Acc: ▓
Epoch 15:
Validation loss stabilizes
Accuracy > 95%
Epoch 42:
Reached 99% accuracy
Learning rate decay
Epoch 67:
Convergence achieved
Final performance
Continuous model improvement through strategic sample selection using uncertainty estimation:
H(y|x) = -Σ P(y|x) log P(y|x)
Entropy-based sample prioritization
Decentralized training while preserving privacy:
wt+1 = wt - η∇L(wt, Dlocal)
Local updates aggregated globally
The HyperDeteX ecosystem is designed to create a self-sustaining environment where all participants benefit from their contributions while collectively improving the platform's capabilities. Our ecosystem integrates various stakeholders through a carefully designed incentive structure.
The DTX token is the backbone of the HyperDeteX ecosystem, designed to incentivize participation, govern the platform, and facilitate value exchange between stakeholders. Our tokenomics model ensures long-term sustainability and alignment of interests.
Total Supply
100M
DTX tokens
Initial Circulation
15%
Of total supply
Vesting Period
4 yrs
Linear release
The HyperDeteX contribution model is designed to maximize community engagement while ensuring the highest quality of data and model improvements. Our framework enables various forms of participation, each with its own reward structure and validation process.
Submit authentic voice recordings for model training
Develop and submit improved detection algorithms
Participate in sample and model validation
Run nodes and maintain network infrastructure
Fixed DTX allocation for accepted contributions
Additional rewards based on contribution impact
Enhanced rewards for long-term participants
Voting power proportional to contribution
Validation Speed
24h
Average review time
Acceptance Rate
82%
Quality submissions
Validator Network
1000+
Active validators
HyperDeteX's technology finds applications across various sectors, providing robust protection against voice-based threats and enabling new possibilities for secure voice authentication and verification.
Direct access to detection services via REST API
Native integration for mobile and web applications
Custom deployment for specific business needs
Our technical roadmap outlines the planned evolution of the HyperDeteX platform, focusing on continuous improvement of detection capabilities, scalability, and user experience.
Ensuring robust protection and system stability
Supporting growing network demands
Streamlining integration and usage
Exploring new AI architectures
Enhancing data protection
Improving system efficiency
HyperDeteX is led by a team of experts in artificial intelligence, blockchain technology, and cybersecurity. Our leadership combines deep technical expertise with extensive industry experience to drive innovation and sustainable growth.
HyperDeteX operates within a comprehensive regulatory framework designed to ensure compliance with international standards while protecting user privacy and data security. Our approach combines proactive regulatory engagement with robust internal controls.
Audit Score
98%
Security rating
Response Time
<24h
Issue resolution
Compliance Rate
100%
Regulatory
Data Protection
A+
Security grade
As voice technology continues to evolve, HyperDeteX is positioned to lead the next wave of innovation in synthetic voice detection and verification. Our vision extends beyond current capabilities to shape the future of secure voice communication.
Market Size
$5.6B
By 2030
User Growth
2,750%
Total growth
Network Nodes
50K+
Target 2030
Partners
500+
Global reach
HyperDeteX is positioned to capitalize on the explosive growth of the voice biometrics and deepfake detection market, projected to reach $5.6 billion by 2030 with a CAGR of 47.6%. As the global AI market expands to $2 trillion and voice authentication becomes standard across financial services, healthcare, and government sectors, HyperDeteX will serve as the critical infrastructure protecting against synthetic voice fraud. Through our decentralized approach and community-driven development, we are building the foundation for trusted voice communication in an AI-dominated future.