624 lines
16 KiB
Python
624 lines
16 KiB
Python
import math
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import random
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from typing import Any
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# 生成类人的鼠标轨迹
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def generate_realistic_trajectory(
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start_x: int, start_y: int, end_x: int, end_y: int, start_time: int
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) -> list[Any]:
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"""生成类人的鼠标轨迹"""
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trajectory = []
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# 计算距离和步数
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distance = ((end_x - start_x) ** 2 + (end_y - start_y) ** 2) ** 0.5
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steps = int(distance / 2) + random.randint(5, 15) # 根据距离动态调整步数
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current_time = start_time
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current_x, current_y = start_x, start_y
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for i in range(steps):
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# 使用贝塞尔曲线模拟自然移动
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progress = i / steps
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# 添加缓动函数（开始快，中间慢，结束快）
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if progress < 0.3:
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eased = progress / 0.3 * 0.2
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elif progress < 0.7:
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eased = 0.2 + (progress - 0.3) / 0.4 * 0.5
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else:
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eased = 0.7 + (progress - 0.7) / 0.3 * 0.3
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# 计算目标位置（添加随机抖动）
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target_x = start_x + (end_x - start_x) * eased
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target_y = start_y + (end_y - start_y) * eased
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# 添加微小的随机偏移（模拟手抖）
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jitter_x = random.uniform(-0.5, 0.5)
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jitter_y = random.uniform(-0.5, 0.5)
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current_x = int(target_x + jitter_x)
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current_y = int(target_y + jitter_y)
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# 随机时间间隔（3-25ms，符合人类反应）
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time_delta = random.choices(
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[3, 4, 5, 6, 7, 8, 10, 12, 15, 20, 24],
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weights=[5, 8, 10, 12, 10, 8, 5, 3, 2, 1, 1],
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)[0]
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current_time += time_delta
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trajectory.append(["move", current_x, current_y, current_time, "pointermove"])
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# 偶尔在同一位置停留（模拟视觉确认）
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if random.random() < 0.15:
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trajectory.append(
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[
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"move",
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current_x,
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current_y,
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current_time + random.randint(5, 15),
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"pointermove",
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]
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)
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current_time += random.randint(5, 15)
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# 到达目标后的悬停
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hover_time = random.randint(50, 150)
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for _ in range(random.randint(2, 5)):
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current_time += random.randint(8, 25)
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trajectory.append(
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[
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"move",
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end_x + random.randint(-1, 1),
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end_y + random.randint(-1, 1),
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current_time,
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"pointermove",
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]
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)
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current_time += hover_time
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# 点击事件
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trajectory.append(["down", end_x, end_y, current_time, "pointerdown"])
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trajectory.append(["focus", current_time + 1])
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click_duration = random.randint(80, 130)
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trajectory.append(["up", end_x, end_y, current_time + click_duration, "pointerup"])
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return trajectory
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# 处理原始轨迹数组
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def process_mouse_trajectory(
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events: list[Any], max_records: int | None = None
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) -> dict[str, Any]:
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"""
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处理鼠标/触摸轨迹数据，将绝对坐标转换为相对坐标和时间差
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参数:
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events: 原始事件数据列表
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max_records: 最大保留记录数（None表示保留全部）
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返回:
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处理后的事件列表
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"""
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if not events or len(events) == 0:
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return {"data": [], "first_event": None, "last_event": None, "total_events": 0}
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# 初始化变量
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prev_x = 0 # 上一个X坐标
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prev_y = 0 # 上一个Y坐标
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prev_time = 0 # 上一个时间戳
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result = [] # 结果数组
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first_event = None # 第一个事件
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last_event = None # 最后一个事件
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# 移动类事件（包含坐标信息）
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MOVE_EVENTS = ["move", "mousemove", "touchmove", "pointermove"]
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# 点击类事件（仅时间信息）
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CLICK_EVENTS = [
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"down",
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"up",
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"click",
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"mousedown",
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"mouseup",
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"touchstart",
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"touchend",
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"pointerdown",
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"pointerup",
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]
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# 特殊事件（仅时间信息）
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TIME_ONLY_EVENTS = ["focus", "blur", "keydown", "keyup"]
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# 如果设置了最大记录数，只处理最后N条
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start_index = 0
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if max_records and len(events) > max_records:
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start_index = len(events) - max_records
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# 遍历事件
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for i in range(start_index, len(events)):
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event = events[i]
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event_type = event[0]
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# 处理移动类事件（包含X, Y坐标）
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if event_type in MOVE_EVENTS:
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x = event[1]
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y = event[2]
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timestamp = event[3]
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# 记录第一个和最后一个事件
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if first_event is None:
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first_event = event
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last_event = event
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# 计算相对坐标差值
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delta_x = x - prev_x
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delta_y = y - prev_y
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# 计算时间差
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if prev_time == 0:
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time_diff = 0 # 第一个事件时间差为0
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else:
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time_diff = timestamp - prev_time
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# 添加到结果数组
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result.append([event_type, [delta_x, delta_y], time_diff])
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# 更新上一次的值
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prev_x = x
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prev_y = y
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prev_time = timestamp
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# 处理点击类事件（包含坐标但只记录时间差）
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elif event_type in CLICK_EVENTS:
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timestamp = event[3] if len(event) > 3 else event[1]
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# 计算时间差
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if prev_time == 0:
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time_diff = 0
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else:
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time_diff = timestamp - prev_time
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# 添加到结果数组（坐标差为[0,0]）
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result.append([event_type, [0, 0], time_diff])
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prev_time = timestamp
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# 处理仅时间类事件（如focus）
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elif event_type in TIME_ONLY_EVENTS:
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timestamp = event[1]
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# 计算时间差
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if prev_time == 0:
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time_diff = 0
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else:
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time_diff = timestamp - prev_time
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# 添加到结果数组（仅包含时间差）
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result.append([event_type, time_diff])
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prev_time = timestamp
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return {
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"data": result,
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"first_event": first_event,
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"last_event": last_event,
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"total_events": len(result),
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}
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def compress_trajectory(e: list[Any]) -> str:
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"""
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压缩轨迹数据的完整实现
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Args:
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e: 轨迹数据列表
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Returns:
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压缩后的 Base64 编码字符串
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"""
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# 事件类型映射
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p = {
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"move": 0,
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"down": 1,
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"up": 2,
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"scroll": 3,
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"focus": 4,
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"blur": 5,
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"unload": 6,
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"unknown": 7,
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}
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def h(e, t):
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"""
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填充二进制字符串
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Args:
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e: 数值
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t: 目标长度
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Returns:
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填充后的二进制字符串
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"""
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n = bin(e)[2:] # 转为二进制并去掉 '0b' 前缀
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r = ""
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o = len(n) + 1
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while o <= t:
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r += "0"
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o += 1
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return r + n
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def f(e):
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"""
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压缩事件类型数组
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Args:
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e: 事件类型列表
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Returns:
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压缩后的二进制字符串
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"""
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t = []
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n = len(e)
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r = 0
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# 游程编码（Run-Length Encoding）
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while r < n:
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o = e[r]
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i = 0
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while True:
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if 16 <= i:
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break
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s = r + i + 1
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if n <= s:
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break
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if e[s] != o:
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break
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i += 1
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r = r + 1 + i
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a = p[o]
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if i != 0:
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t.append(8 | a) # 设置重复标志位
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t.append(i - 1)
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else:
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t.append(a)
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# 编码长度信息
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_ = h(32768 | n, 16)
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c = ""
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for l in range(len(t)):
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c += h(t[l], 4)
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return _ + c
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def c(e, t):
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"""
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对数组每个元素应用函数
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Args:
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e: 输入数组
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t: 转换函数
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Returns:
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转换后的数组
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"""
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n = []
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for r in range(len(e)):
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n.append(t(e[r]))
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return n
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def d(e, t):
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"""
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压缩数值数组（游程编码 + 变长编码）
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Args:
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e: 数值数组
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t: 是否为坐标数据（需要过滤符号位）
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Returns:
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压缩后的二进制字符串
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"""
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# 第一步：限制数值范围到 [-32767, 32767]
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def limit_value(val):
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limit = 32767
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return max(-limit, min(limit, val))
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e = c(e, limit_value)
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# 第二步：游程编码（Run-Length Encoding）
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n = len(e)
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r = 0
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o = []
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while r < n:
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i = 1
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s = e[r]
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a = abs(s)
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# 统计连续相同的值
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while r + i < n and e[r + i] == s and a < 127 and i < 127:
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i += 1
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if i > 1:
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# 重复值编码格式：
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# 位15: 符号标志 (1=负数49152, 0=正数32768)
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# 位14-7: 重复次数 (i)
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# 位6-0: 绝对值 (a)
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o.append((49152 if s < 0 else 32768) | (i << 7) | a)
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else:
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o.append(s)
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r += i
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e = o
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# 第三步：变长编码
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r = [] # 存储每个数字的十六进制位数
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o = [] # 存储实际数值
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for val in e:
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# 计算需要多少个十六进制位（每位4 bit）
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if val == 0:
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bits = 1
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else:
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# 方法1：使用对数（与原JS一致）
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bits = math.ceil(math.log(abs(val) + 1) / math.log(16))
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# 方法2：直接计算（更快）
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# bits = len(format(abs(val), 'x'))
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bits = max(1, bits)
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r.append(h(bits - 1, 2)) # 2位十六进制存储位数信息
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o.append(h(abs(val), 4 * bits)) # 实际值
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i = "".join(r) # 元数据
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s = "".join(o) # 数据
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# 第四步：符号位编码（仅用于坐标数据）
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if t:
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# 过滤掉：0值 和 已编码符号的压缩值（位15=1）
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filtered = [x for x in e if x != 0 and (x >> 15) != 1]
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n = "".join(["1" if x < 0 else "0" for x in filtered])
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else:
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n = ""
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# 最终格式：[头部16位][元数据][数据][符号位]
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# 头部：最高位置1 + 数组长度
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return h(32768 | len(e), 16) + i + s + n
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# 主函数：数据分离
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t = [] # 事件类型
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n = [] # 时间差
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r = [] # X坐标
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o = [] # Y坐标
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for i in range(len(e)):
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a = e[i]
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length = len(a)
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t.append(a[0])
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n.append(a[1] if length == 2 else a[2])
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if length == 3:
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r.append(a[1][0])
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o.append(a[1][1])
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# 压缩各部分
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c_str = f(t) + d(n, False) + d(r, True) + d(o, True)
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# 填充到6的倍数
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l = len(c_str)
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if l % 6 != 0:
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c_str += h(0, 6 - l % 6)
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# Base64编码
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def u(e):
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"""Base64编码"""
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t = ""
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n = len(e) // 6
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base64_chars = (
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"()*,-./0123456789:?@ABCDEFGHIJKLMNOPQRSTUVWXYZ_abcdefghijklmnopqrstuvwxyz~"
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)
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for r in range(n):
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# 每次取6位二进制
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binary_str = e[6 * r : 6 * (r + 1)]
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index = int(binary_str, 2)
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t += base64_chars[index]
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return t
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return u(c_str)
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class TrajectoryEncoder:
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def __init__(self) -> None:
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self.CHARSET = (
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"()*,-./0123456789:?@ABCDEFGHIJKLMNOPQRSTUVWXYZ_abcdefghijklmnopqr"
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)
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self.BASE = len(self.CHARSET) # 64
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self.DIRECTION_CHARS = "stuvwxyz~"
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self.DIRECTION_PATTERNS = [
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[1, 0], # s
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[2, 0], # t
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[1, -1], # u
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[1, 1], # v
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[0, 1], # w
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[0, -1], # x
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[3, 0], # y
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[2, -1], # z
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[2, 1], # ~
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]
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def encode_number(self, num):
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"""编码单个数值为64进制"""
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abs_num = abs(num)
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high_index = abs_num // self.BASE
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low_index = abs_num % self.BASE
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result = ""
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# 负数标记
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if num < 0:
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result += "!"
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# 高位（当值>=64时）
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if high_index > 0 and high_index < self.BASE:
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result += "$"
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result += self.CHARSET[high_index]
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# 低位
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result += self.CHARSET[low_index]
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return result
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def compress_trajectory(self, points):
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"""压缩轨迹：计算相邻点差值"""
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compressed = []
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time_accumulator = 0
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for i in range(len(points) - 1):
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dx = points[i + 1][0] - points[i][0] # 不要用abs
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dy = points[i + 1][1] - points[i][1] # 不要用abs
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dt = abs(points[i + 1][2] - points[i][2]) # 时间可以用abs
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# 跳过完全相同的点
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if dx == 0 and dy == 0 and dt == 0:
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continue
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# 位置不变只累积时间
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if dx == 0 and dy == 0:
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time_accumulator += dt
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else:
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compressed.append([dx, dy, dt + time_accumulator])
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time_accumulator = 0
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# 处理剩余时间
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if time_accumulator != 0:
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compressed.append([0, 0, time_accumulator])
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return compressed
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def get_direction_code(self, dx, dy):
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"""识别是否匹配方向模式"""
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for i, pattern in enumerate(self.DIRECTION_PATTERNS):
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if dx == pattern[0] and dy == pattern[1]:
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return self.DIRECTION_CHARS[i]
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return None
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def encode(self, trajectory):
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"""
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编码轨迹
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trajectory: [[x, y, timestamp], ...]
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返回: 编码后的字符串
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"""
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compressed = self.compress_trajectory(trajectory)
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x_encoded = []
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y_encoded = []
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t_encoded = []
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for dx, dy, dt in compressed:
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direction_code = self.get_direction_code(dx, dy)
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||
if direction_code:
|
||
# 匹配到方向模式，只记录y
|
||
y_encoded.append(direction_code)
|
||
else:
|
||
# 不匹配，完整编码x和y
|
||
x_encoded.append(self.encode_number(dx))
|
||
y_encoded.append(self.encode_number(dy))
|
||
|
||
# 时间总是编码
|
||
t_encoded.append(self.encode_number(dt))
|
||
|
||
# 拼接：x坐标 !! y坐标 !! 时间戳
|
||
return (
|
||
"".join(x_encoded) + "!!" + "".join(y_encoded) + "!!" + "".join(t_encoded)
|
||
)
|
||
|
||
def encrypt_string(self, e, t, n):
|
||
"""
|
||
JS加密函数的Python实现
|
||
|
||
参数:
|
||
e: 原始字符串
|
||
t: 加密参数数组
|
||
n: 十六进制字符串
|
||
"""
|
||
if not t or not n:
|
||
return e
|
||
|
||
o = 0 # 偏移量
|
||
i = e # 结果字符串
|
||
s = t[0] # 12
|
||
a = t[2] # 98
|
||
_ = t[4] # 43
|
||
|
||
# 每次读取2个字符（十六进制）
|
||
while o < len(n):
|
||
r = n[o : o + 2] # 取2个字符
|
||
if len(r) < 2:
|
||
break
|
||
o += 2
|
||
|
||
# 解析十六进制
|
||
c = int(r, 16)
|
||
|
||
# 转换为字符
|
||
l = chr(c)
|
||
|
||
# 计算插入位置: (s * c^2 + a * c + _) % len(e)
|
||
u = (s * c * c + a * c + _) % len(e)
|
||
|
||
# 在位置u插入字符
|
||
i = i[:u] + l + i[u:]
|
||
|
||
return i
|
||
|
||
|
||
def H(t: int, e: str) -> str:
|
||
# 解析后缀
|
||
n = e[-2:]
|
||
r = []
|
||
for char in n:
|
||
o = ord(char)
|
||
r.append(o - 87 if o > 57 else o - 48)
|
||
n = 36 * r[0] + r[1]
|
||
|
||
# 计算目标值
|
||
a = (t) + n
|
||
|
||
# 构建字符池
|
||
_ = [[], [], [], [], []]
|
||
c = {}
|
||
u = 0
|
||
for char in e[:-2]:
|
||
if char not in c:
|
||
c[char] = 1
|
||
_[u].append(char)
|
||
u = (u + 1) % 5
|
||
|
||
# 生成结果
|
||
f = a
|
||
d = 4
|
||
p = ""
|
||
g = [1, 2, 5, 10, 50]
|
||
|
||
while f > 0:
|
||
if f >= g[d]:
|
||
h = int(random.random() * len(_[d]))
|
||
p += _[d][h]
|
||
f -= g[d]
|
||
else:
|
||
_.pop(d)
|
||
g.pop(d)
|
||
d -= 1
|
||
|
||
return p
|