""" Chinese slang / evasion lexicon layer. General-purpose models do not recognise livestream-style slang like 收米 / 上车 / 日结, so this layer scores it with a plain word table. Three steps: 1. normalize() folds the text — fullwidth to halfwidth, zero-width characters stripped, homoglyphs restored (Cyrillic / Greek / lookalike Han characters), traditional to simplified, lowercased. 2. strip_separators() additionally removes in-word separators, which breaks the "split the word up" trick (叚*币 / 假-币 / 假 币). 3. score() matches the table on all three tracks and adds two bonuses: - combo bonus: a money/scam term AND a contact-evasion term in one message - evasion bonus: the term only matches AFTER normalizing / de-separating Usage (called before the judge in bot.py): s, terms = lexicon.score(text) if s >= config.LEXICON_HARD_THRESHOLD: # hard hit — spam, no AI call needed ... else: # soft hit — feed terms to the AI ... The table is extensible through config.LEXICON_EXTRA, so an operator can add terms without touching code. Why the evasion bonus exists ---------------------------- **Deliberate obfuscation is itself evidence of intent.** Somebody discussing or complaining about counterfeit money has no reason to write 假 as 叚. Making that substitution means the sender knows the word gets blocked — which is an admission that what they are posting is the thing that gets blocked. So the same word carries a completely different risk depending on how it was written: "假币" → could be news, a complaint, a question → weight 4, under the hard threshold, goes to the AI for context "叚*币" → 4 + evasion bonus 3 = 7 → over the threshold, deleted at once This does not depend on enumerating every lookalike character: swap in any rare character or insert any separator and, as long as the folded text spells the term, the bonus applies automatically. """ import re import unicodedata import config # Zero-width / directional control characters (spam inserts these mid-word to # break keyword matching). _ZERO_WIDTH = dict.fromkeys( map(ord, "​‌‍‎‏‪‫‬⁠"), None ) # Common homoglyphs: Cyrillic / Greek → Latin (letters disguised as English). _HOMOGLYPH = { "а": "a", "е": "e", "о": "o", "р": "p", "с": "c", "х": "x", "у": "y", "ѕ": "s", "і": "i", "ј": "j", "к": "k", "н": "h", "в": "b", "м": "m", "т": "t", "ο": "o", "ρ": "p", "α": "a", "ν": "v", "τ": "t", "ϲ": "c", } # Lookalike Han characters / traditional / Japanese shinjitai → simplified. # NFKC does **nothing** for these: it handles fullwidth and compatibility forms, # not distinct characters that merely look alike, and it does no traditional → # simplified conversion. 叚 (U+53DA) and 假 (U+5047) are two separate characters. _CJK_HOMOGLYPH = { # Lookalike substitutions actually observed in spam "叚": "假", "仮": "假", "葭": "假", "帀": "币", "巿": "币", # Traditional / variant → simplified (outside NFKC's remit) "幣": "币", "鈔": "钞", "偽": "伪", "僞": "伪", "貨": "货", "錢": "钱", "髙": "高", "證": "证", "護": "护", "銀": "银", "帳": "账", "號": "号", "軟": "软", "體": "体", "電": "电", "報": "报", "聯": "联", "係": "系", "繫": "系", "點": "点", "擊": "击", "賣": "卖", "買": "买", "貸": "贷", } # Characters commonly pushed into the middle of a word as separators (the * in # 叚*币). Stripping zero-width characters is not enough — these are visible # characters and NFKC leaves them alone. _SEPARATORS = re.compile(r"[\*\-_\.·・~｜|/\\\s、，,。:：;；'\"“”‘’()（）\[\]【】<>《》!！?？#＃]+") def normalize(text): """Fullwidth → halfwidth, drop zero-width, restore homoglyphs, lowercase.""" if not text: return "" t = unicodedata.normalize("NFKC", text) # fullwidth → halfwidth t = t.translate(_ZERO_WIDTH) # drop zero-width/marks t = "".join(_HOMOGLYPH.get(ch, ch) for ch in t) # Cyrillic/Greek → Latin t = "".join(_CJK_HOMOGLYPH.get(ch, ch) for ch in t) # lookalike Han → simplified return t.lower() def strip_separators(text): """Remove in-word separators, defeating 叚*币 / 假-币 / 假 币 style splitting. This crosses legitimate punctuation boundaries ("美国。币安" → "美国币安"), so it is used **only as an auxiliary match track and every hit on it carries the evasion bonus** — it is never a verdict on its own. """ return _SEPARATORS.sub("", text or "") # Table: term -> (meaning, weight, category) # Categories: money=earning/recruiting contact=contact-detail evasion # pay=payment/crypto scam=fraud scheme # fake=counterfeit currency/documents (serious, and the ad format is # "the account itself is the contact detail") # Higher weight = more suspicious. A single term is usually not enough to call # something spam on its own (see LEXICON_HARD_THRESHOLD) — combinations and the # threshold keep false positives down (收米, for example, is an ordinary word in a # livestream-tipping context). SLANG = { # ---- Earning / recruiting ---- "收米": ("收钱", 3, "money"), "上车": ("入局/加入项目", 2, "money"), "车头": ("项目发起人", 2, "money"), "带单": ("带人下注/投资", 3, "money"), "日结": ("日结工资(刷单诈骗常见)", 2, "money"), "日入": ("日收入(夸张收益诱导)", 2, "money"), "刷单": ("刷单兼职诈骗", 3, "money"), "兼职": ("兼职引流", 1, "money"), "口子": ("放贷/诈骗渠道", 3, "money"), "洗码": ("赌场洗码", 3, "money"), "跑分": ("跑分洗钱", 3, "money"), "卡商": ("贩卖银行卡/账号", 3, "money"), "杀猪盘": ("杀猪盘诈骗", 3, "scam"), "反水": ("赌博返利", 3, "money"), "包赢": ("赌博诱导", 3, "scam"), "稳赚": ("虚假收益", 2, "scam"), "躺赚": ("虚假收益", 2, "scam"), "内部消息": ("荐股诈骗", 2, "scam"), "带你飞": ("带单诱导", 2, "money"), # ---- Counterfeit currency ---- # Serious offence, but the plain spelling gets weight 4 (under the hard # threshold) so context still goes to the AI; obfuscated it picks up the # evasion bonus and hard-hits on its own. # Note 冥币 (joss paper, a funeral good) is deliberately NOT in this table. "假币": ("伪造货币", 4, "fake"), "假钞": ("伪造钞票", 4, "fake"), "伪钞": ("伪造钞票", 4, "fake"), "假钱": ("伪造货币", 3, "fake"), # These are pure trade jargon — they do not turn up in ordinary conversation, # so one occurrence is enough. Weight 6 clears the threshold by itself. "高仿钞": ("高仿伪钞", 6, "fake"), "仿真钞": ("仿真伪钞", 6, "fake"), "1:1真钞": ("伪钞话术", 6, "fake"), # 练功券 is a bank note-counting practice pad — a legal product — so it only # gets 4 and the AI decides from context. "练功券": ("点钞练习券(常被用作伪钞幌子)", 4, "fake"), # ---- Contact-detail evasion ---- "薇": ("微信", 2, "contact"), "威": ("微信", 2, "contact"), "维": ("微信", 2, "contact"), "魏": ("微信", 2, "contact"), "vx": ("微信", 2, "contact"), "vxin": ("微信", 2, "contact"), "威信": ("微信", 2, "contact"), "扣扣": ("QQ", 2, "contact"), "企鹅": ("QQ", 2, "contact"), "扣v": ("加QQ/微信", 2, "contact"), "纸飞机": ("Telegram", 2, "contact"), "电报": ("Telegram", 1, "contact"), # A bare 飞机 cannot go in — "我坐飞机去北京" would be a false positive. Only # the multi-character forms that unambiguously mean a contact handle. "飞机号": ("Telegram 账号", 2, "contact"), "联系飞机": ("Telegram 联系", 2, "contact"), "飞机搜": ("Telegram 搜索", 2, "contact"), "tg号": ("Telegram 账号", 2, "contact"), "电报号": ("Telegram 账号", 2, "contact"), "蝙蝠": ("BatChat 加密聊天", 2, "contact"), "皮皮虾": ("加密聊天软件", 2, "contact"), "私我": ("私聊引流", 1, "contact"), "详聊": ("私下详谈引流", 1, "contact"), "加我": ("引流加好友", 1, "contact"), # ---- Payment / crypto ---- "usdt": ("USDT 加密货币支付", 2, "pay"), "泰达": ("USDT", 2, "pay"), "承兑": ("加密货币承兑洗钱", 3, "pay"), "代收": ("第三方代收款", 2, "pay"), "四方": ("四方支付(灰产收款)", 3, "pay"), } # Evasion bonus: added when a term only matches after normalizing / removing # separators. 3 is chosen so a single obfuscated fake-currency term (weight 4) # clears the default hard threshold of 6, while the same term spelled plainly # stays at 4 and still goes to the AI. _EVASION_BONUS = 3 def score(text): """Return (total, ['term=meaning', ...]). Each term scores at most once. Three match tracks: raw the original text, lowercased only — direct hit, no bonus norm after normalize() — needed folding = evasion strip after normalize() + strip_separators() — same """ if not text: return 0, [] raw = text.lower() norm = normalize(text) strip = strip_separators(norm) if not norm: return 0, [] table = dict(SLANG) extra = getattr(config, "LEXICON_EXTRA", None) or {} table.update(extra) total = 0 matched = [] cats = set() for term, meta in table.items(): meaning, weight, cat = meta hit_raw = term in raw if not (hit_raw or term in norm or term in strip): continue total += weight cats.add(cat) if hit_raw: matched.append(term + "=" + meaning) else: # Only matched after folding → the sender obfuscated it on purpose, # which is evidence of intent in itself. total += _EVASION_BONUS matched.append(term + "=" + meaning + " (obfuscated)") # Combo bonus: an earning/scam/counterfeit term together with a # contact-evasion term is strongly suspicious. if cats & {"money", "scam", "pay", "fake"} and "contact" in cats: total += 3 return total, matched def is_hard_spam(text): """Normalized score >= the hard threshold (config.LEXICON_HARD_THRESHOLD, default 6) → spam outright. Used by the prefilter: a hard slang hit saves an AI call and defeats fullwidth / lookalike / zero-width / split-word evasion.""" s, _ = score(text) return s >= getattr(config, "LEXICON_HARD_THRESHOLD", 6)