Face recognition for an introduction to Python penetration testing

Recently, I received a network security book "Python Black Hat" presented by the Electronic Industry Press, with a total of 24 experiments in the book, and today reproduces the 11th experiment (face recognition), my test environment is MBP computer + Conda development environment. This experiment is immediately followed by the previous traffic image restoration, and the traffic image restoration can be connected to the man-in-the-middle attack, so that you can sniff out what content the target is browsing, such as pornographic websites, so as to create a breakthrough for social engineering fraud and extortion~

AILX10

Excellent answerer in cybersecurity

Master's in Cybersecurity

Go to consult

1. First go to the Internet to search for a few face pictures, I originally planned to put all the pictures of beautiful women, here to pay tribute to the industry bosses

@腹黑

、

@kn1f3

2. Download the required XML configuration file

wget https://eclecti.cc/files/2008/03/haarcascade_frontalface_alt.xml           

3. Run the script on MBP

4. View the face result in the faces folder

Reference Code:

# -*- coding: utf-8 -*-
# @Time    : 2022/6/13 7:36 PM
# @Author  : ailx10
# @File    : detector.py

import cv2
import os

ROOT = "/Users/ailx10/py3hack/chapter4/picture"
FACES = "/Users/ailx10/py3hack/chapter4/faces"
TRAIN = "/Users/ailx10/py3hack/chapter4/training"

def detect(srcdir=ROOT,tgtdir=FACES,train_dir=TRAIN):
    for fname in os.listdir(srcdir):
        if not fname.upper().endswith(".JPG"):
            continue
        fullname = os.path.join(srcdir,fname)
        newname = os.path.join(tgtdir,fname)
        img = cv2.imread(fullname)
        if img is None:
            continue
        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        training = os.path.join(train_dir,"haarcascade_frontalface_alt.xml")
        cascade = cv2.CascadeClassifier(training)
        rects = cascade.detectMultiScale(gray,1.3,5)
        try:
            if rects.any():
                print("Got a face")
                rects[:,2:] += rects[:,:2]
        except AttributeError:
            print(f"No face found in {fname}")
            continue

        for x1,y1,x2,y2 in rects:
            cv2.rectangle(img,(x1,y1),(x2,y2),(127,255,0),2)
        cv2.imwrite(newname,img)

if __name__ == "__main__":
    detect()           

Posted on 2022-06-13 19:52