# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 2 Related Works  
A problem in defining the search string consisted of the significant variability of terms used to refer to the addressed subject. For example, terms sometimes used to refer to counterfeits are \"falsification\", \"tampering\", \"counterfeiting\", \"adulteration\", \"forgery\", \"manipulation\", \"edited\", \"doctored\" or \"altered\". After some tests, we defined the following search string: \"image AND (tamper OR forged OR forgery) AND (detect OR localize) AND NOT video\". This search returned a considerable amount of work. To delimit the search, using the first search string, we adopted the following criteria:  
* The presented detection method must follow the passive approach;
* The Detection method should primarily focus on verifying digital images' authenticity; and
* Must be in the top five most relevant results of each base that follows the other criteria. With this research, we find 15 works. However, most of the papers returned on this initial search did not claim to be capable of detecting all types of manipulation and primarily focused on a single type, therefore, to include works related to general identification, terms such as \"global\" or \"universal\" were tested. Unfortunately, not all works that detect all types of manipulation used those terms, so the search key had to be made less specific: \"image AND (tamper OR forged OR forgery) AND (detect OR localize)\" and to limit the search the following criteria were employed:  
* Introduce a method of detecting general-purpose manipulated images in their text;
* The presented detection method must follow the passive approach;
* The presented method should not require file formats with data compression;
* The Detection method should primarily focus on verifying digital images' authenticity;
* The paper was published in the last five years; and
* The paper must have the most relevant results of each base that follow the other criteria. From this search, three additional works were incorporated, bringing the total to 18 selected works. Among the selected works, similarities emerge in their operational methodologies, despite variances in their detection approaches. We identified commonalities based on the detected manipulations, denoted as Det. Man. (D for duplication, S for splicing, and R for retouching with a slash used to distinguish them where applicable), their treatment of color space, techniques employed for feature extraction to extract pertinent image details, and the detection methods utilized to identify image manipulation. These shared characteristics are meticulously detailed in Table 1, which serves as a comprehensive overview of their collective attributes and methodologies. The detection methods found can be categorized as classifiers capable of determining alterations in JPEG artifacts, classifiers to detect duplicated areas, and methods that compare detected features to determine correlation or fixed pattern noise (FPN). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & **Det. ** & \multirow{2}{*}{**Color Space**} & \multirow{2}{*}{**Feature Extraction**} & \multirow{2}{*}{**Detection**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**References**} \\  & **Man. ** & & & & & & \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{D/S} & YCbCr, Y values & Blocks with DCT using doubly stochastic model & Classifiers of type: SVM and ELM & CASIA(V1,V2) & [19] \\  & & & Keypoints by proposed & & Correlation of characteristics & MICC-F220, & \\ \multirow{2}{*}{2} & \multirow{2}{*}{D} & \multirow{2}{*}{Grayscale} & method and local & by: angle and distance & MICC-F600, CMH & [5] \\  & & & & & & \\  & & & &  
To understand methods based on fixed pattern noise, it is first necessary to understand the image capture process. Image acquisition aims to transform an image into a discrete and numerical representation that a computer can store and process.