Generating large-scale noise data is usually used in scenarios such as testing, data enhancement, privacy protection, or confrontation training. The following is the automated generation method of sub-scenes, with specific examples and precautions： ###1. Text noise generation 1. **Random string generation** ```python import random import string def generate_random_text(num_lines=10000, line_length=50): with open('noise_text. txt', 'w') as f: for _ in range(num_lines): line = ''. join(random. choices(string. printable, k=line_length)) f. write(line + '\n') # Generate 100,000 lines of noise with invisible characters generate_random_text(100000, 128) ``` 2. **Natural language pollution (for NLP scenarios)** ```python from transformers import GPT2Tokenizer, GPT2LMHeadModel import torch tokenizer = GPT2Tokenizer. from_pretrained('gpt2') model = GPT2LMHeadModel. from_pretrained('gpt2') def generate_seminoise(text, mutation_rate=0.3): tokens = tokenizer. encode(text) # Randomly replace 30% of tokens for i in range(len(tokens)): if random. random() < mutation_rate: tokens[i] = random. randint(100, 50256) #token range of GPT-2 return tokenizer. decode(tokens) # Example: Turn normal text into semantic noise print (generate_seminoise ("The weather is so nice today")) ``` ###2. Numerical noise generation 1. **Multi-dimensional time series data pollution** ```python import numpy as np import pandas as pd def generate_time_series_noise(samples=1e6, dimensions=10): # Generate noise that conforms to the distribution of real data timestamps = pd. date_range(start='2020-01-01', periods=samples, freq='S') data = np. random. normal(loc=0, scale=5, size=(samples, dimensions)) data += np. sin(np. linspace(0, 20*np. pi, samples))[:,None]# Add periodic characteristics return pd. DataFrame(data, index=timestamps, columns=[f'sensor_{i}' for i in range(dimensions)]) # Generate million-level timing noise df = generate_time_series_noise(1_000_000) df.to_parquet('sensor_noise.parquet') ``` ###3. Image noise generation (advanced method) 1. **Confrontational noise generation** ```python import torch import torch. nn as nn from torchvision import transforms class AdversarialNoiseGenerator(nn. Module): def __init__(self, target_model): super(). __init__() self. noise = nn. Parameter(torch. randn(1, 3, 224, 224)*0.1) self. target_model = target_model def forward(self, clean_images): noisy_images = clean_images + self. noise return self. target_model(noisy_images) # Example of use： pretrained_model = torch. hub. load('pytorch/vision', 'resnet50', pretrained=True) adv_generator = AdversarialNoiseGenerator(pretrained_model) optimizer = torch. optim. Adam(adv_generator. parameters(), lr=0.01) # Optimize the noise interference model to predict for _ in range(1000): clean_img = torch. rand(1, 3, 224, 224) # Analog input predictions = adv_generator(clean_img) loss = -torch. mean(predictions) #Maximize prediction error loss. backward() optimizer. step() ``` ###4. Audio noise generation 1. **Frequency domain pollution algorithm** ```python import librosa import soundfile as sf import numpy as np def spectral_contamination(input_audio, noise_level=0.3): y, sr = librosa. load(input_audio) D = librosa. stft(y) magnitude, phase = librosa. magphase(D) # Add peak noise in key frequency bands noise = np. random. laplace(scale=noise_level, size=magnitude. shape) noise[:, 100:200] *= 5 # Enhanced mid-band interference contaminated = magnitude + noise return librosa. istft(contaminated * phase) # Generate noise with frequency domain characteristics noisy_audio = spectral_contamination("clean. wav") sf. write('contaminated. wav', noisy_audio, 22050) ``` ###5. Efficient generation skills 1. **Distributed generation (Dask example)** ```python import dask from dask. distributed import Client client