mirror of https://gitlab.com/bashrc2/epicyon
376 lines
14 KiB
Python
376 lines
14 KiB
Python
""" Decoy location metadata on images.
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An aim of this is to reinforce confirmation bias within machine learning
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systems looking for patterns.
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"""
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__filename__ = "city.py"
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__author__ = "Bob Mottram"
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__license__ = "AGPL3+"
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__version__ = "1.5.0"
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__maintainer__ = "Bob Mottram"
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__email__ = "bob@libreserver.org"
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__status__ = "Production"
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__module_group__ = "Metadata"
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import os
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import datetime
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import random
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import math
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from random import randint
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from utils import acct_dir
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from utils import remove_eol
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# states which the simulated city dweller can be in
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PERSON_SLEEP = 0
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PERSON_WORK = 1
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PERSON_PLAY = 2
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PERSON_SHOP = 3
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PERSON_EVENING = 4
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PERSON_PARTY = 5
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BUSY_STATES = (PERSON_WORK, PERSON_SHOP, PERSON_PLAY, PERSON_PARTY)
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def _get_decoy_camera(decoy_seed: int) -> (str, str, int):
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"""Returns a decoy camera make and model which took the photo
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"""
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cameras = [
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["Apple", "iPhone SE"],
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["Apple", "iPhone XR"],
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["Apple", "iPhone 8"],
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["Apple", "iPhone 11"],
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["Apple", "iPhone 11 Pro"],
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["Apple", "iPhone 12"],
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["Apple", "iPhone 12 Mini"],
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["Apple", "iPhone 12 Pro Max"],
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["Apple", "iPhone 13"],
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["Apple", "iPhone 13 Mini"],
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["Apple", "iPhone 13 Pro"],
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["Apple", "iPhone 14"],
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["Apple", "iPhone 14 Pro"],
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["Apple", "iPhone 15"],
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["Apple", "iPhone 15 Pro"],
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["Samsung", "Galaxy S24 Ultra"],
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["Samsung", "Galaxy S24 Plus"],
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["Samsung", "Galaxy S24"],
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["Samsung", "Galaxy S23 Plus"],
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["Samsung", "Galaxy S23"],
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["Samsung", "Galaxy S22 Plus"],
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["Samsung", "Galaxy S22"],
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["Samsung", "Galaxy S21 Ultra"],
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["Samsung", "Galaxy S21"],
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["Samsung", "Galaxy Note 20 Ultra"],
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["Samsung", "Galaxy S20 Plus"],
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["Samsung", "Galaxy S20 FE 5G"],
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["Samsung", "Galaxy Z FOLD 2"],
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["Samsung", "Galaxy S12 Plus"],
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["Samsung", "Galaxy S12"],
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["Samsung", "Galaxy S11 Plus"],
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["Samsung", "Galaxy Z Flip"],
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["Samsung", "Galaxy A54"],
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["Samsung", "Galaxy A51"],
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["Samsung", "Galaxy A60"],
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["Samsung", "Note 13"],
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["Samsung", "Note 13 Plus"],
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["Samsung", "Note 12"],
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["Samsung", "Note 12 Plus"],
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["Samsung", "Note 11"],
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["Samsung", "Note 11 Plus"],
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["Samsung", "Note 10"],
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["Samsung", "Note 10 Plus"],
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["Samsung", "Galaxy Note 20 Ultra"],
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["Samsung", "Galaxy S20 FE"],
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["Samsung", "Galaxy Z Fold 2"],
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["Samsung", "Galaxy A52 5G"],
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["Samsung", "Galaxy A71 5G"],
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["Google", "Pixel 8 Pro"],
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["Google", "Pixel 8a"],
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["Google", "Pixel 8"],
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["Google", "Pixel 7 Pro"],
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["Google", "Pixel 7"],
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["Google", "Pixel 6 Pro"],
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["Google", "Pixel 6"],
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["Google", "Pixel 5"],
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["Google", "Pixel 4a"],
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["Google", "Pixel 4 XL"],
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["Google", "Pixel 3 XL"],
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["Google", "Pixel 4"],
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["Google", "Pixel 4a 5G"],
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["Google", "Pixel 3"],
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["Google", "Pixel 3a"]
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]
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randgen = random.Random(decoy_seed)
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index = randgen.randint(0, len(cameras) - 1)
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serial_number = randgen.randint(100000000000, 999999999999999999999999)
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return cameras[index][0], cameras[index][1], serial_number
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def _get_city_pulse(curr_time_of_day, decoy_seed: int) -> (float, float):
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"""This simulates expected average patterns of movement in a city.
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Jane or Joe average lives and works in the city, commuting in
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and out of the central district for work. They have a unique
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life pattern, which machine learning can latch onto.
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This returns a polar coordinate for the simulated city dweller:
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Distance from the city centre is in the range 0.0 - 1.0
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Angle is in radians
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"""
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randgen = random.Random(decoy_seed)
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variance = 3
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data_decoy_state = PERSON_SLEEP
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weekday = curr_time_of_day.weekday()
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min_hour = 7 + randint(0, variance)
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max_hour = 17 + randint(0, variance)
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if curr_time_of_day.hour > min_hour:
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if curr_time_of_day.hour <= max_hour:
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if weekday < 5:
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data_decoy_state = PERSON_WORK
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elif weekday == 5:
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data_decoy_state = PERSON_SHOP
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else:
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data_decoy_state = PERSON_PLAY
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else:
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if weekday < 5:
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data_decoy_state = PERSON_EVENING
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else:
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data_decoy_state = PERSON_PARTY
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randgen2 = random.Random(decoy_seed + data_decoy_state)
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angle_radians = \
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(randgen2.randint(0, 100000) / 100000) * 2 * math.pi
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# some people are quite random, others have more predictable habits
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decoy_randomness = randgen.randint(1, 3)
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# occasionally throw in a wildcard to keep the machine learning guessing
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if randint(0, 100) < decoy_randomness:
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distance_from_city_center = randint(0, 100000) / 100000
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angle_radians = (randint(0, 100000) / 100000) * 2 * math.pi
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else:
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# what consitutes the central district is fuzzy
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central_district_fuzz = (randgen.randint(0, 100000) / 100000) * 0.1
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busy_radius = 0.3 + central_district_fuzz
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if data_decoy_state in BUSY_STATES:
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# if we are busy then we're somewhere in the city center
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distance_from_city_center = \
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(randgen.randint(0, 100000) / 100000) * busy_radius
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else:
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# otherwise we're in the burbs
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distance_from_city_center = busy_radius + \
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((1.0 - busy_radius) * (randgen.randint(0, 100000) / 100000))
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return distance_from_city_center, angle_radians
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def parse_nogo_string(nogo_line: str) -> []:
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"""Parses a line from locations_nogo.txt and returns the polygon
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"""
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nogo_line = remove_eol(nogo_line)
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polygon_str = nogo_line.split(':', 1)[1]
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if ';' in polygon_str:
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pts = polygon_str.split(';')
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else:
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pts = polygon_str.split(',')
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if len(pts) <= 4:
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return []
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polygon = []
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for index in range(int(len(pts)/2)):
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if index*2 + 1 >= len(pts):
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break
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longitude_str = pts[index*2].strip()
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latitude_str = pts[index*2 + 1].strip()
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if 'E' in latitude_str or 'W' in latitude_str:
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longitude_str = pts[index*2 + 1].strip()
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latitude_str = pts[index*2].strip()
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if 'E' in longitude_str:
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longitude_str = \
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longitude_str.replace('E', '')
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longitude = float(longitude_str)
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elif 'W' in longitude_str:
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longitude_str = \
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longitude_str.replace('W', '')
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longitude = -float(longitude_str)
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else:
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longitude = float(longitude_str)
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latitude = float(latitude_str)
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polygon.append([latitude, longitude])
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return polygon
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def spoof_geolocation(base_dir: str,
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city: str, curr_time, decoy_seed: int,
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cities_list: [],
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nogo_list: []) -> (float, float, str, str,
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str, str, int):
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"""Given a city and the current time spoofs the location
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for an image
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returns latitude, longitude, N/S, E/W,
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camera make, camera model, camera serial number
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"""
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locations_filename = base_dir + '/custom_locations.txt'
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if not os.path.isfile(locations_filename):
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locations_filename = base_dir + '/locations.txt'
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nogo_filename = base_dir + '/custom_locations_nogo.txt'
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if not os.path.isfile(nogo_filename):
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nogo_filename = base_dir + '/locations_nogo.txt'
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man_city_radius = 0.1
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variance_at_location = 0.0004
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default_latitude = 51.8744
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default_longitude = 0.368333
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default_latdirection = 'N'
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default_longdirection = 'W'
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if cities_list:
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cities = cities_list
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else:
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if not os.path.isfile(locations_filename):
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return (default_latitude, default_longitude,
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default_latdirection, default_longdirection,
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"", "", 0)
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cities = []
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try:
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with open(locations_filename, 'r', encoding='utf-8') as loc_file:
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cities = loc_file.readlines()
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except OSError:
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print('EX: unable to read locations ' + locations_filename)
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nogo = []
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if nogo_list:
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nogo = nogo_list
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else:
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if os.path.isfile(nogo_filename):
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nogo_list = []
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try:
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with open(nogo_filename, 'r', encoding='utf-8') as nogo_file:
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nogo_list = nogo_file.readlines()
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except OSError:
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print('EX: spoof_geolocation unable to read ' + nogo_filename)
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for line in nogo_list:
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if line.startswith(city + ':'):
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polygon = parse_nogo_string(line)
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if polygon:
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nogo.append(polygon)
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city = city.lower()
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for city_name in cities:
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if city in city_name.lower():
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city_fields = city_name.split(':')
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latitude = city_fields[1]
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longitude = city_fields[2]
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area_km2 = 0
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if len(city_fields) > 3:
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area_km2 = int(city_fields[3])
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latdirection = 'N'
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longdirection = 'E'
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if 'S' in latitude:
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latdirection = 'S'
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latitude = latitude.replace('S', '')
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if 'W' in longitude:
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longdirection = 'W'
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longitude = longitude.replace('W', '')
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latitude = float(latitude)
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longitude = float(longitude)
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# get the time of day at the city
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approx_time_zone = int(longitude / 15.0)
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if longdirection == 'E':
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approx_time_zone = -approx_time_zone
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curr_time_adjusted = curr_time - \
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datetime.timedelta(hours=approx_time_zone)
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cam_make, cam_model, cam_serial_number = \
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_get_decoy_camera(decoy_seed)
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valid_coord = False
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seed_offset = 0
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while not valid_coord:
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# patterns of activity change in the city over time
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(distance_from_city_center, angle_radians) = \
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_get_city_pulse(curr_time_adjusted,
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decoy_seed + seed_offset)
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# The city radius value is in longitude and the reference
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# is Manchester. Adjust for the radius of the chosen city.
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if area_km2 > 1:
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man_radius = math.sqrt(1276 / math.pi)
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radius = math.sqrt(area_km2 / math.pi)
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city_radius_deg = (radius / man_radius) * man_city_radius
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else:
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city_radius_deg = man_city_radius
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# Get the position within the city, with some randomness added
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latitude += \
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distance_from_city_center * city_radius_deg * \
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math.cos(angle_radians)
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longitude += \
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distance_from_city_center * city_radius_deg * \
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math.sin(angle_radians)
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longval = longitude
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if longdirection == 'W':
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longval = -longitude
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valid_coord = not point_in_nogo(nogo, latitude, longval)
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if not valid_coord:
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seed_offset += 1
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if seed_offset > 100:
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break
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# add a small amount of variance around the location
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fraction = randint(0, 100000) / 100000
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distance_from_location = fraction * fraction * variance_at_location
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fraction = randint(0, 100000) / 100000
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angle_from_location = fraction * 2 * math.pi
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latitude += distance_from_location * math.cos(angle_from_location)
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longitude += distance_from_location * math.sin(angle_from_location)
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# gps locations aren't transcendental, so round to a fixed
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# number of decimal places
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latitude = int(latitude * 100000) / 100000.0
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longitude = int(longitude * 100000) / 100000.0
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return (latitude, longitude, latdirection, longdirection,
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cam_make, cam_model, cam_serial_number)
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return (default_latitude, default_longitude,
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default_latdirection, default_longdirection,
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"", "", 0)
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def get_spoofed_city(city: str, base_dir: str,
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nickname: str, domain: str) -> str:
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"""Returns the name of the city to use as a GPS spoofing location for
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image metadata
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"""
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city = ''
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city_filename = acct_dir(base_dir, nickname, domain) + '/city.txt'
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if os.path.isfile(city_filename):
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try:
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with open(city_filename, 'r', encoding='utf-8') as city_file:
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city1 = city_file.read()
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city = remove_eol(city1)
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except OSError:
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print('EX: get_spoofed_city unable to read ' + city_filename)
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return city
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def _point_in_polygon(poly: [], x_coord: float, y_coord: float) -> bool:
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"""Returns true if the given point is inside the given polygon
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"""
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num = len(poly)
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inside = False
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p2x = 0.0
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p2y = 0.0
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xints = 0.0
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p1x, p1y = poly[0]
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for i in range(num + 1):
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p2x, p2y = poly[i % num]
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if y_coord > min(p1y, p2y):
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if y_coord <= max(p1y, p2y):
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if x_coord <= max(p1x, p2x):
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if p1y != p2y:
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xints = \
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(y_coord - p1y) * (p2x - p1x) / (p2y - p1y) + p1x
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if p1x == p2x or x_coord <= xints:
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inside = not inside
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p1x, p1y = p2x, p2y
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return inside
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def point_in_nogo(nogo: [], latitude: float, longitude: float) -> bool:
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"""Returns true of the given geolocation is within a nogo area
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"""
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for polygon in nogo:
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if _point_in_polygon(polygon, latitude, longitude):
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return True
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return False
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