Ubuntu
447 lines
17 KiB
Python
447 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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Anti-AI Detector - 去AI化工具
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支持中文、英文和小红书风格优化
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"""
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import re
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import os
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import sys
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import argparse
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from pathlib import Path
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from typing import Dict, List, Tuple
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class AntiAIDetector:
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"""去AI化检测器"""
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def __init__(self):
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self.init_cn_patterns()
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self.init_en_patterns()
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self.init_xhs_patterns()
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# ==================== 中文AI痕迹模式 ====================
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def init_cn_patterns(self):
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"""初始化中文AI检测模式"""
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self.cn_patterns = {
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# 标志性短语
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"值得注意的是": ["需要关注的是", "关键点在于", ""],
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"我们可以看到": ["从数据看", "实际情况是", ""],
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"换句话说": ["也就是说", "简单来说", ""],
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"不难发现": ["可以看出", "", ""],
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"显而易见": ["很明显", "显然", ""],
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"众所周知": ["大家都知道", "", ""],
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"综上所述": ["总结来说", "", ""],
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"归根结底": ["根本原因是", "说到底", ""],
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"由此可见": ["这说明", "", ""],
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# 结构模式
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"首先": ["第一", "开始", ""],
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"其次": ["第二", "然后", ""],
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"最后": ["最终", "", ""],
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"总而言之": ["总的来说", "", ""],
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# 过度修饰
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"非常": ["很", "挺", ""],
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"极其": ["很", "特别", ""],
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"毫无疑问": ["确实", "", ""],
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"一定程度上": ["", "", ""],
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# 模板化表达
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"随着...的发展": ["", "", ""],
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"在当今社会": ["", "", ""],
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"引起了广泛关注": ["", "", ""],
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"具有重要的意义": ["", "", ""],
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}
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self.cn_structures = [
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# 排比结构
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(r'不仅[^，]+，而且[^，]+，还[^。]+', "避免三重排比"),
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(r'一方面[^，]+，另一方面[^，]+', "减少固定结构"),
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# 过渡词堆砌
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(r'首先.*?其次.*?最后.*?(综上所述|总而言之)', "去除模板化结构"),
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# 学术腔
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(r'从[^,]+角度(来看|分析|出发)', "口语化表达"),
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(r'基于[^,]+(的)?(背景|基础|之上)', "简化表达"),
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]
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# ==================== 英文AI痕迹模式（Stop Slop）====================
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def init_en_patterns(self):
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"""初始化英文AI检测模式（基于Stop Slop）"""
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self.en_patterns = {
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# 标志性短语
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"Here's the thing": ["The point is", "Fact is", ""],
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"Here's the deal": ["The reality is", "", ""],
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"Let that sink in": ["", "", ""],
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"The uncomfortable truth is": ["Truth is", "", ""],
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"The reality is": ["", "", ""],
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"This matters because": ["Because", "", ""],
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"What matters is": ["", "", ""],
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"Full stop": ["", "", ""],
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"Period": ["", "", ""],
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"Let me be clear": ["", "", ""],
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"I'm going to be honest": ["", "", ""],
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"Make no mistake": ["", "", ""],
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# 过度使用的动词
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"navigate": ["handle", "deal with", "work through"],
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"unpack": ["explain", "break down", ""],
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"leverage": ["use", "take advantage of", ""],
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"delve": ["explore", "look at", ""],
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"dive into": ["explore", "examine", ""],
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"peel back the layers": ["examine", "analyze", ""],
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# 固定结构
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"Not because": ["Not from", "", ""],
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"But because": ["But from", "", ""],
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"That's it": ["", "", ""],
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"That's the": ["", "", ""],
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"And that's okay": ["", "", ""],
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# 过度修饰
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"In today's world": ["Today", "Now", ""],
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"In the digital age": ["", "", ""],
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"Rapidly evolving": ["changing", "", ""],
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"Fast-paced": ["busy", "", ""],
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# 模糊表达
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"It's important to note": ["Note", "", ""],
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"It's worth mentioning": ["", "", ""],
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"As mentioned earlier": ["", "", ""],
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}
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self.en_structures = [
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# "Not because X. Because Y."
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(r'Not because[^.]+\. Because[^.]+\.', "合并为一句"),
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# "[Noun]. That's it. That's the [thing]."
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(r'(\w+)\. That\'s it\. That\'s the[^.]+\.', "简化表达"),
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# 三重形容词
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(r'\w+, \w+, and \w+ (landscape|environment|space)', "减少形容词"),
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# 过度使用hedging
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(r'arguably|potentially|likely|generally|typically', "直接表达"),
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]
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# ==================== 小红书风格优化 ====================
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def init_xhs_patterns(self):
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"""初始化小红书风格检测模式"""
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self.xhs_patterns = {
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# 去除营销感
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"强烈推荐": ["推荐", "我觉得", ""],
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"必买": ["值得买", "可以考虑", ""],
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"神器": ["好用", "不错", ""],
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"绝绝子": ["很好", "不错", ""],
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"YYDS": ["很棒", "", ""],
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"宝藏": ["好", "不错", ""],
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"闭眼入": ["推荐", "", ""],
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"天花板": ["很好", "顶级", ""],
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# 去模板化
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"今天给大家分享": ["分享", "说说", ""],
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"姐妹们": ["大家", "", ""],
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"干货": ["内容", "", ""],
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"码住": ["收藏", "记住", ""],
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"抄作业": ["参考", "", ""],
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# 过度emoji提示
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"emoji堆砌": ["适量使用emoji", "", ""],
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"标签堆砌": ["减少标签", "", ""],
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}
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self.xhs_structures = [
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# 过于规整的排版
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(r'^[0-9]+[.、\s]', "避免过于规整的序号"),
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(r'✅|❌|⚠️|⭐|🌟{3,}', "emoji过多"),
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(r'#[^#\s]+#[^#\s]+#[^#\s]+', "标签过多"),
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]
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# ==================== 检测方法 ====================
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def detect_cn(self, text: str) -> List[Tuple[str, str, List[str]]]:
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"""检测中文AI痕迹"""
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findings = []
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for pattern, replacements in self.cn_patterns.items():
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if pattern in text or re.search(pattern.replace('...', '.*?'), text):
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findings.append(("phrase", pattern, replacements))
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for pattern, suggestion in self.cn_structures:
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matches = re.findall(pattern, text, re.DOTALL)
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if matches:
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findings.append(("structure", pattern, [suggestion]))
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return findings
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def detect_en(self, text: str) -> List[Tuple[str, str, List[str]]]:
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"""检测英文AI痕迹"""
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findings = []
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text_lower = text.lower()
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for pattern, replacements in self.en_patterns.items():
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if pattern.lower() in text_lower:
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findings.append(("phrase", pattern, replacements))
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for pattern, suggestion in self.en_structures:
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matches = re.findall(pattern, text, re.IGNORECASE)
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if matches:
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findings.append(("structure", pattern, [suggestion]))
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return findings
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def detect_xhs(self, text: str) -> List[Tuple[str, str, List[str]]]:
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"""检测小红书风格问题"""
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findings = []
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for pattern, replacements in self.xhs_patterns.items():
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if pattern in text:
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findings.append(("xhs_phrase", pattern, replacements))
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for pattern, suggestion in self.xhs_structures:
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matches = re.findall(pattern, text, re.MULTILINE)
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if matches:
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findings.append(("xhs_structure", pattern, [suggestion]))
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# 检测emoji密度
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emoji_count = len(re.findall(r'[\U0001F600-\U0001F64F\U0001F300-\U0001F5FF\U0001F680-\U0001F6FF\U0001F1E0-\U0001F1FF]', text))
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if emoji_count > 5:
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findings.append(("emoji_density", f"{emoji_count}个emoji", ["建议减少到3-5个"]))
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return findings
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# ==================== 处理方法 ====================
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def humanize_cn(self, text: str, aggressive: bool = False) -> str:
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"""中文去AI化"""
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result = text
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# 替换标志性短语
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replacements = {
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"值得注意的是": "",
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"我们可以看到": "",
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"不难发现": "",
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"显而易见": "很明显",
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"众所周知": "大家都知道",
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"综上所述": "总结来说",
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"归根结底": "说到底",
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"由此可见": "这说明",
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"换句话说": "也就是说",
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}
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for old, new in replacements.items():
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if new:
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result = result.replace(old, new)
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else:
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# 删除或根据上下文调整
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result = result.replace(old + "，", "")
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result = result.replace(old + ",", "")
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# 简化过渡词
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result = re.sub(r'首先[，,]?', "第一，", result)
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result = re.sub(r'其次[，,]?', "第二，", result)
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result = re.sub(r'最后[，,]?', "最后，", result)
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# 去除过度修饰
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result = re.sub(r'非常([的地])', r'很\1', result)
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result = re.sub(r'极其([的地])', r'特别\1', result)
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# 去除模板化开头
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result = re.sub(r'随着[^，]+的发展[，,]', "", result)
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result = re.sub(r'在当今社会[，,]', "", result)
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if aggressive:
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# 更激进的优化
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result = re.sub(r'[，,]+', "，", result) # 合并多个逗号
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result = re.sub(r'。+', "。", result) # 合并多个句号
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return result.strip()
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def humanize_en(self, text: str, aggressive: bool = False) -> str:
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"""英文去AI化（基于Stop Slop）"""
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result = text
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# 替换标志性短语
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replacements = {
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r"Here's the thing[:,.]*\s*": "",
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r"Here's the deal[:,.]*\s*": "",
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r"Let that sink in[:,.]*\s*": "",
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r"The uncomfortable truth is[:,.]*\s*": "",
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r"Let me be clear[:,.]*\s*": "",
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r"I'm going to be honest[:,.]*\s*": "",
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r"Make no mistake[:,.]*\s*": "",
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r"Full stop[.]*": ".",
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r"Period[.]*": ".",
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# 过度使用的动词
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r"navigate\s+(the\s+)?": "handle ",
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r"unpack\s*": "explain ",
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r"leverage\s*": "use ",
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r"delve\s+(into\s+)?": "explore ",
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r"dive into\s*": "explore ",
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}
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for pattern, replacement in replacements.items():
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result = re.sub(pattern, replacement, result, flags=re.IGNORECASE)
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# 简化结构
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result = re.sub(r'Not because([^,.]+)[,.]\s*Because([^,.]+)[,.]',
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r'Not from\1, but from\2.', result, flags=re.IGNORECASE)
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result = re.sub(r"(\w+)\.\s*That's it\.\s*That's the[^.]+\.",
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r'\1 is what matters.', result, flags=re.IGNORECASE)
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# 去除过度修饰
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result = re.sub(r'In today\'s world[,\s]*', '', result, flags=re.IGNORECASE)
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result = re.sub(r'In the digital age[,\s]*', '', result, flags=re.IGNORECASE)
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result = re.sub(r'rapidly evolving', 'changing', result, flags=re.IGNORECASE)
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result = re.sub(r'fast-paced', 'busy', result, flags=re.IGNORECASE)
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# 简化hedging
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result = re.sub(r'It\'s important to note that\s*', '', result, flags=re.IGNORECASE)
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result = re.sub(r'It\'s worth mentioning that\s*', '', result, flags=re.IGNORECASE)
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if aggressive:
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# 去除更多学术腔
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result = re.sub(r'arguably\s*[,]*\s*', '', result, flags=re.IGNORECASE)
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result = re.sub(r'potentially\s*[,]*\s*', '', result, flags=re.IGNORECASE)
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return result.strip()
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def humanize_xhs(self, text: str) -> str:
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"""小红书风格优化"""
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result = text
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# 替换营销词汇
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replacements = {
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"强烈推荐": "推荐",
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"必买": "值得买",
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"闭眼入": "推荐",
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"神器": "好用",
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"绝绝子": "很好",
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"YYDS": "很棒",
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"宝藏": "不错",
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"天花板": "顶级",
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"干货": "内容",
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"码住": "收藏",
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"抄作业": "参考",
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}
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for old, new in replacements.items():
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result = result.replace(old, new)
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# 简化称呼
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result = result.replace("姐妹们", "大家")
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result = result.replace("集美们", "大家")
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result = result.replace("宝子们", "大家")
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# 优化emoji分布（保留但不过度）
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# 如果emoji过多，提示用户手动调整
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# 减少标签堆砌
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result = re.sub(r'#{3,}', '#', result)
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# 去除过于规整的序号
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result = re.sub(r'^\d+[.、]\s*', '', result, flags=re.MULTILINE)
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return result.strip()
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def auto_detect_and_humanize(self, text: str) -> Tuple[str, str]:
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"""自动检测语言并去AI化"""
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# 简单语言检测
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chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text))
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english_chars = len(re.findall(r'[a-zA-Z]', text))
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if chinese_chars > english_chars:
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# 中文为主
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return self.humanize_cn(text), "zh"
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else:
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# 英文为主
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return self.humanize_en(text), "en"
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def analyze(self, text: str) -> Dict:
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"""全面分析文本的AI痕迹"""
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cn_findings = self.detect_cn(text)
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en_findings = self.detect_en(text)
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xhs_findings = self.detect_xhs(text)
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return {
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"cn_findings": cn_findings,
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"en_findings": en_findings,
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"xhs_findings": xhs_findings,
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"cn_score": len(cn_findings),
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"en_score": len(en_findings),
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"xhs_score": len(xhs_findings),
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"total_risk": len(cn_findings) + len(en_findings) + len(xhs_findings),
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}
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def main():
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"""命令行入口"""
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parser = argparse.ArgumentParser(description='去AI化工具')
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parser.add_argument('text', nargs='?', help='输入文本')
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parser.add_argument('-f', '--file', help='输入文件')
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parser.add_argument('-o', '--output', help='输出文件')
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parser.add_argument('-t', '--type', choices=['zh', 'en', 'xhs', 'auto'],
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default='auto', help='处理类型')
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parser.add_argument('-a', '--aggressive', action='store_true',
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help='激进模式')
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parser.add_argument('--analyze', action='store_true',
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help='仅分析不处理')
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args = parser.parse_args()
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detector = AntiAIDetector()
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# 获取输入文本
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if args.file:
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with open(args.file, 'r', encoding='utf-8') as f:
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text = f.read()
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elif args.text:
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text = args.text
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else:
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print("请提供文本或使用-f指定文件")
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return
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# 分析模式
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if args.analyze:
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result = detector.analyze(text)
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print("=" * 50)
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print("AI痕迹分析报告")
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print("=" * 50)
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print(f"中文AI痕迹: {result['cn_score']} 处")
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print(f"英文AI痕迹: {result['en_score']} 处")
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print(f"小红书风格问题: {result['xhs_score']} 处")
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print(f"总体风险: {result['total_risk']} 处")
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print("\n详细发现:")
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for finding in result['cn_findings'][:5]:
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print(f" [CN] {finding[1]}")
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for finding in result['en_findings'][:5]:
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print(f" [EN] {finding[1]}")
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return
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# 处理模式
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if args.type == 'auto':
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result, lang = detector.auto_detect_and_humanize(text)
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print(f"检测到语言: {lang}")
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elif args.type == 'zh':
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result = detector.humanize_cn(text, args.aggressive)
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elif args.type == 'en':
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result = detector.humanize_en(text, args.aggressive)
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elif args.type == 'xhs':
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result = detector.humanize_xhs(text)
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||
# 输出结果
|
||
if args.output:
|
||
with open(args.output, 'w', encoding='utf-8') as f:
|
||
f.write(result)
|
||
print(f"结果已保存到: {args.output}")
|
||
else:
|
||
print("\n" + "=" * 50)
|
||
print("处理结果:")
|
||
print("=" * 50)
|
||
print(result)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|