Find near duplicate and faked images-CodePudding

The

Image 2 (b1.jpg):

The difference between the hashing score of these images is : 24

When pixeled (50x50 pixels), they look like this:

rs_a1.jpg

rs_b1.jpg

The hashing difference score of the pixeld images is even bigger! : 26

Below two more examples of near duplicate image pairs as requested by @ann zen:

Pair 1

Pair 2

The code I use to reduce the image size is this :

from PIL import Image with Image.open(image_path) as image: reduced_image = image.resize((50, 50)).convert('RGB').convert("1")

And the code for comparing two image hashing:

from PIL import Image import imagehash with Image.open(image1_path) as img1: hashing1 = imagehash.phash(img1) with Image.open(image2_path) as img2: hashing2 = imagehash.phash(img2) print('difference : ', hashing1-hashing2)

CodePudding user response：

Pixel-based approach is oversimplification, because you are talking about object-similarity, not pixel similarity. You need deep-learning feature values, not pixel values.

I have collected a

img1_2.jpg & img2_2.jpg:

img1_3.jpg & img2_3.jpg:

To prove that the blur doesn't produce really off false-positives, I ran this program:

import cv2 import numpy as np def process(img): h, w, _ = img.shape img = cv2.resize(img, (350, h * w // 350)) img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) return cv2.GaussianBlur(img_gray, (43, 43), 21) def confidence(img1, img2): res = cv2.matchTemplate(process(img1), process(img2), cv2.TM_CCOEFF_NORMED) return res.max() img1s = list(map(cv2.imread, ["img1_1.jpg", "img1_2.jpg", "img1_3.jpg"])) img2s = list(map(cv2.imread, ["img2_1.jpg", "img2_2.jpg", "img2_3.jpg"])) for i, img1 in enumerate(img1s, 1): for j, img2 in enumerate(img2s, 1): conf = confidence(img1, img2) print(f"img1_{i} img2_{j} Confidence: {round(conf * 100, 2)}%")

Output:

img1_1 img2_1 Confidence: 84.2% # Corresponding images img1_1 img2_2 Confidence: -10.86% img1_1 img2_3 Confidence: 16.11% img1_2 img2_1 Confidence: -2.5% img1_2 img2_2 Confidence: 84.61% # Corresponding images img1_2 img2_3 Confidence: 43.91% img1_3 img2_1 Confidence: 14.49% img1_3 img2_2 Confidence: 59.15% img1_3 img2_3 Confidence: 87.25% # Corresponding images

Notice how only when matching the images with their corresponding images does the program output high confidence levels (84 %).