Digital image modification detection using color information and its histograms

Abstract

The rapid development of many open source and commercial image editing software makes the authenticity of the digital images questionable. Copy-move forgery is one of the most widely used tampering techniques to create desirable objects or conceal undesirable objects in a scene. Existing techniques reported in the literature to detect such tampering aim to improve the robustness of these methods against the use of JPEG compression, blurring, noise, or other types of post processing operations. These post processing operations are frequently used with the intention to conceal tampering and reduce tampering clues. A robust method based on the color moments and other five image descriptors is proposed in this paper. The method divides the image into fixed size overlapping blocks. Clustering operation divides entire search space into smaller pieces with similar color distribution. Blocks from the tampered regions will reside within the same cluster since both copied and moved regions have similar color distributions. Five image descriptors are used to extract block features, which makes the method more robust to post processing operations. An ensemble of deep compositional pattern-producing neural networks are trained with these extracted features. Similarity among feature vectors in clusters indicates possible forged regions. Experimental results show that the proposed method can detect copy-move forgery even if an image was distorted by gamma correction, addictive white Gaussian noise, JPEG compression, or blurring.

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

“…ey used accuracy ratio, which is the same of the verification rate in equation 2, in order to test their technique. ey tested their technique against the free-form copy-move forgeries which include no postprocessing, and they state that they found their accuracy ratio higher than 0.95 [20]. Since there is no more information about their success rate on CoMoFoD dataset, we could not make a graphical comparison between their study and ours.…”

“…While 128 of these images were generated by using original images acquired from internet, the remaining 32 of them were generated by using an original image from our camera. Besides, so as to compare the results of the proposed technique with the results of the other studies [18][19][20], we also tested our technique on 64 free-form copymove forgery images which originate from CoMoFoD dataset [21]. During the test operations, in order to measure the strength of the forgery detection algorithm against the postprocessing operations which aim at making the detection of the forgery harder, the test images were exposed to JPEG compression, Gaussian filtering, and different combinations of these two operations.…”

Optimalisasi Penyusutan Exif Metadata Dengan Teknik Substitusi Null Value Pada Kasus Keamanan Citra Digital

“…Data citra digital (metadata) memiliki informasi yang sangat kompleks, meliputi informasi perangkat pengambilan gambar sampai dengan waktu kapan diambil (Zhou et al, 2016). Sehingga dapat dikatakan metadata citra digital tersebut dapat digunakan sebagai hak cipta.…”

Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

“…The most typical features are the frequency representation, such as the histogram (e.g. [38]), or the Discrete Cosine Transform (DCT) (e.g. [39][40]) of the blocks.…”

“…An example of an effective matching method is clustering: the search space is divided into regions with similar features-vector distributions (e.g. [38]). Another popular feature matching is sorting.…”