Detection of Free-Form Copy-Move Forgery on Digital Images

Abstract

Nowadays, production and distribution of digital images has become part of our life. Since digital images, which are important carriers of information, are considered as the concrete proofs of facts in many fields and they can be used as evidence in the courts of law, development of techniques to ensure image authenticity is an active research topic. Copy-move forgery is one of the most common manipulation techniques that are implemented on the digital images, and various techniques have been developed for detection of these kinds of forgeries. JPEG format, which presents the ability of making high rate compression without causing remarkable changes in the meaning of the image, is the most commonly used format on digital images. In this study, the topic of detecting free-form copy-move forgeries on digital images is covered. It has been observed that the developed technique is able to detect the professional forgeries in which the copied region is selected in free-form and which are almost impossible to be detected by human eye, with high success rate, and it is able to give successful results even if the image is exposed to postprocesses such as JPEG compression and Gaussian filtering, which make the detection of forgery harder.

Comparison of specific segmentation methods used for copy move detection

“…In Figure 6, the images used were included JPEG compression images with different compression factors also followed by applying Gaussian filtering. As shown from the Figures 4 to 6 the verification rates and accuracies for CMFD with no postprocessing and JPEG compression and also with only Gaussian filtering or Gaussian filtering afterward JPEG compression and with JPEG compression afterward Gaussian filtering for the proposed work were approximately similar to the results obtained in [56] and k-means achieved better than SLIC-DBSCAN with some images but error rate (Er) and false-positive rate (FPr) for the proposed work was better than the results obtained in [56] which means that the total number of pixels that are forged region pairs but detected as forged by the proposed work was smaller and this also reflects on the error rates.…”

“…To measure the effectiveness of the proposed algorithms the following parameters calculated: i) true positive (TP) which refers to the total number of pixels that are forged regions and recognized by the algorithms as forged regions; ii) true negative (TN) which refers to the total number of pixels that are forged regions but recognized by the algorithm as not forged regions; iii) false positive (FP) which refers to the total number of pixels that are not forged regions but recognized by the algorithm as forged regions; iv) false negative (FN) which refers to the total number of pixels that are not forged regions and recognized by the algorithm as not forged regions. Then the evaluation metrics calculated according to the previous measures as described in the (2)-( 5) [56]. To test the proposed work, the dataset named free-form copy-move dataset [57] is used.…”

OTKRIVANJE NAMERNO IZAZVANIH PROMENA NA SLICI KORIŠĆENJEM SOFTVERA Fiji, ImageJ i SPSS-a

“…Copy move forgery detection (CMFD)) deo slike je kopiran i zalepljen na drugi deo slike [1]. Metode detekcije za ovakve vrste falsifikovanja slike uglavnom su kategorisane u pristupe zasnovane na blokovima [2] i pristupe zasnovane na ključnim tačkama [3,4]. U pristupu zasnovanom na blokovima, slika je podeljena na mala preklapanja ili nepreklapanja blokova.…”

Detection of GAN-Synthesized Image Based on Discrete Wavelet Transform

“…We cannot believe our eyes anymore in the media. [17][18][19] usually analyze the traces inducted in image synthesis and inspect the pixel-level disparities in real and fake images. Compared with traditional fake images, GAN-synthesized images have better quality, and no traces are inducted in image mosaic.…”