pic_carver.py

import re
import zlib
import cv2
from scapy.all import *

pictures_directory = "/home"
faces_directory = "/home"

pcap_file = "bhp.pcap"

def get_http_headers(http_payload):
    try:
        # split the headers off if it is HTTP traffic
        headers_raw = http_payload[:http_payload.index("\r\n\r\n")+2]
        # break out the headers
        headers = dict(re.findall(r"(?P<name>.*?): (?P<value>.*?)\r\n", ¬
        headers_raw))
    except:
        return None
    if "Content-Type" not in headers:
return None
return headers

def extract_image(headers,http_payload):
    image = None
    image_type = None
    try:
        if "image" in headers['Content-Type']:
            # grab the image type and image body
            image_type = headers['Content-Type'].split("/")[1]
            image = http_payload[http_payload.index("\r\n\r\n")+4:]

            # if we detect compression decompress the image
            try:
                if "Content-Encoding" in headers.keys():
                    if headers['Content-Encoding'] == "gzip":
                        image = zlib.decompress(image, 16+zlib.MAX_WBITS)
                    elif headers['Content-Encoding'] == "deflate":
                        image = zlib.decompress(image)
            except:
                pass
    except:
        return None,None
return image,image_type

def http_assembler(pcap_file):
    carved_images = 0
    faces_detected = 0

    a = rdpcap(pcap_file)

    sessions = a.sessions()

    for session in sessions:
        http_payload = ""
        for packet in sessions[session]:
            try:
                if packet[TCP].dport == 80 or packet[TCP].sport == 80:
                    # reassemble the stream
                    http_payload += str(packet[TCP].payload)
            except:
                pass
        headers = get_http_headers(http_payload)
        if headers is None:
            continue
        image,image_type = extract_image(headers,http_payload)
        if image is not None and image_type is not None:
        # store the image
        file_name = "%s-pic_carver_%d.%s" % (pcap_file,carved_images,image_type)
        fd = open("%s/%s" % (pictures_directory,file_name),"wb")
        fd.write(image)
        fd.close()
        carved_images += 1
        # now attempt face detection
        try:
            result = face_detect("%s/%s" % (pictures_directory,file_name),file_name)
            if result is True:
                faces_detected += 1
        except:
            pass
return carved_images, faces_detected

carved_images, faces_detected = http_assembler(pcap_file)
print "Extracted: %d images" % carved_images
print "Detected: %d faces" % faces_detected

def face_detect(path,file_name):
    img = cv2.imread(path)
    cascade = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
    rects = cascade.detectMultiScale(img, 1.3, 4, cv2.cv.CV_HAAR_¬
    SCALE_IMAGE, (20,20))
    if len(rects) == 0:
        return False
    rects[:, 2:] += rects[:, :2]
    # highlight the faces in the image
    for x1,y1,x2,y2 in rects:
        cv2.rectangle(img,(x1,y1),(x2,y2),(127,255,0),2)
    cv2.imwrite("%s/%s-%s" % (faces_directory,pcap_file,file_name),img)
    return True