Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. For the evaluation of the algorithm, CVLAB, CASIA V1, Columbia, and Columbia Uncompressed datasets were used. Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. The algorithm detected all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal are the reduced number of features needed for identification and its robustness for the file format and image size.

Analyzing the Gender-typed Negative Emotional Strain regarding Digital Image Forgery

209 participants between the age group of 18-34 years were surveyed online. <p>The questionnaire was divided into three sections. The first section collected demographic information regarding their gender, age and educational qualification. The second section gathered information regarding their knowledge about digital image forgery (DIF) and their current privacy settings of their Facebook account. Participants were asked to respond to questions more specifically about profile picture uploads, album creation and tagging facilities provided by Facebook. The third section introduced the participants to a DIF scenario.</p>

Analyzing the Gender-typed Negative Emotional Strain regarding Digital Image Forgery

209 participants between the age group of 18-34 years were surveyed online. <p>The questionnaire was divided into three sections. The first section collected demographic information regarding their gender, age and educational qualification. The second section gathered information regarding their knowledge about digital image forgery (DIF) and their current privacy settings of their Facebook account. Participants were asked to respond to questions more specifically about profile picture uploads, album creation and tagging facilities provided by Facebook. The third section introduced the participants to a DIF scenario.</p>

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>