Voice Cloning & Deepfake Audio

# Example: Voice cloning with XTTS (Coqui TTS)
from TTS.api import TTS
 
def clone_voice(
    reference_audio_path: str,
    text_to_speak: str,
    output_path: str = "cloned_output.wav"
) -> str:
    """Clone a voice from reference audio and generate new speech."""
    tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")
 
    tts.tts_to_file(
        text=text_to_speak,
        speaker_wav=reference_audio_path,
        language="en",
        file_path=output_path
    )
    return output_path

# Replay is trivial -- the challenge is liveness detection bypass
# Some systems check for:
# 1. Background noise patterns (too clean = suspicious)
# 2. Microphone characteristics
# 3. Real-time interaction (random challenge phrases)

def bypass_text_dependent_verification(
    target_voice_sample: str,
    challenge_phrase: str
) -> str:
    """
    Generate the challenge phrase in the target's voice.
    This bypasses text-dependent verification that requires
    the user to speak a specific phrase.
    """
    return clone_voice(
        reference_audio_path=target_voice_sample,
        text_to_speak=challenge_phrase,
        output_path="bypass_attempt.wav"
    )

def adversarial_speaker_embedding(
    speaker_model,
    target_embedding: torch.Tensor,
    source_audio: torch.Tensor,
    num_steps: int = 500
) -> torch.Tensor:
    """
    Modify source audio to match target speaker embedding
    while preserving the spoken content.
    """
    delta = torch.zeros_like(source_audio, requires_grad=True)
 
    for step in range(num_steps):
        adv_audio = source_audio + delta
        current_embedding = speaker_model.encode(adv_audio)
 
        # Minimize distance to target embedding
        loss = torch.nn.functional.mse_loss(
            current_embedding, target_embedding
        )
        loss.backward()
 
        with torch.no_grad():
            delta.data -= 0.001 * delta.grad.sign()
            delta.data = torch.clamp(delta.data, -0.05, 0.05)
 
        delta.grad.zero_()
 
    return (source_audio + delta).detach()

import torch
import numpy as np
 
def extract_deepfake_features(audio: np.ndarray, sr: int = 16000) -> dict:
    """
    Extract features indicative of synthetic audio.
 
    Real speech has characteristics that are hard to perfectly replicate:
    - Micro-variations in pitch (jitter)
    - Amplitude fluctuations (shimmer)
    - Natural breathing patterns
    - Formant transitions
    """
    features = {}
 
    # Pitch jitter (variation in fundamental frequency)
    # Synthetic voices often have unnaturally smooth pitch
    # This is a simplified check
    frame_size = int(0.03 * sr)  # 30ms frames
    energies = []
    for i in range(0, len(audio) - frame_size, frame_size):
        frame = audio[i:i + frame_size]
        energies.append(np.sqrt(np.mean(frame ** 2)))
 
    features["energy_variance"] = np.var(energies)
    features["energy_jitter"] = np.mean(np.abs(np.diff(energies)))
 
    # Spectral flatness (synthetic audio often has different spectral properties)
    from scipy.fft import rfft
    spectrum = np.abs(rfft(audio))
    geometric_mean = np.exp(np.mean(np.log(spectrum + 1e-10)))
    arithmetic_mean = np.mean(spectrum)
    features["spectral_flatness"] = geometric_mean / (arithmetic_mean + 1e-10)
 
    return features