# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 2 Related Works
This approach requires a sensor capable of capturing a range of energy from the electromagnetic spectrum and generating as output an electric signal proportional to the captured energy level; then, a digitizer must convert the analog signal into digital information that can be represented in binary form. The manufacturing process of most sensors responsible for capturing images in digital cameras introduces imperfections that cause minor differences in light sensitivity [31]. The divergences of all the sensors present in a camera introduce a variation in the values of the pixels of images registered by these cameras, resulting in unevenness similar to a signature in all the images it generates [32]. The FPN of a sensor is constant; however, it varies from sensor to sensor. In Sensors of the Charged-Coupled Device (CCD) type, the FPN varies randomly, while in sensors of the Complementary Metal Oxide Semiconductor (CMOS) type, due to its perpendicular capture system, the FPN forms vertical bars, as can be seen in Figure 1 taken from [33]. The work by [25] assumes that the FPN information of the camera used for capture is previously known and then calculates the FPN of the image for validation by comparing the two and marking significantly different areas as being possibly altered. On the other hand, methods based on classifiers use filtered features to determine if there is a correlation between parts of the image or alteration of the compression through machine learning [34]. In deep learning [35; 36; 37], convolutional neural networks (CNNs) are increasingly being used for image classification [38; 39; 40]. In this field, several authors are working to reduce the complexity of the classification step, considering the use of big data [41; 42; 43] and making this evaluation more efficient [44; 45; 46]. Finally, correlation-based methods compare features by similarity to determine whether they contain Duplication, often using a final step to eliminate a portion of the found correlations. In this context, unsupervised learning methods are also applied [47]. All datasets in our literature review focus on detecting Duplication and Splicing manipulations. Their most significant divergences are the images used for alteration, the size of the altered area, and the application of subsequent modifications to hide falsification on the manipulated areas. The work by [48], which is the only one focused on detecting manipulation retouching, had to algorithmically generate its dataset. The datasets used by analyzed works focused only on the detection of Duplication type are MICC-F220, MICC-F600, MICC-F, MICC-F2000, MICC-F8 multi, IMD, CMH, CoMoFoD, and UCID. The datasets encompassing Duplication and Splicing manipulations consist of CASIA v1.0, CASIA v2.0, Columbia, and the Image Manipulation Database. CASIA v1.0 and v2.0  
Figure 1: Comparison of FPN present in CCD and CMOS sensors. (**left**) FPN of a CCD sensor (**right**) FPN pf a CMOS sensor. feature manually tampered images with splicing and duplication manually introduced. In contrast, the Columbia and Image Manipulation Dataset are generated by algorithmically by editing patches of an image. The Columbia dataset exclusively contains splicing changes, while the Image Manipulation Database includes both duplication and splicing. Both datasets feature different alterations classified as retouching, aimed at camouflaging other modifications. The absence of a standardized dataset across these investigations poses challenges in comparing the effectiveness of methods solely based on reported accuracy metrics. Additionally, the reliability of results obtained on datasets algorithmically generated by manipulating image patches may introduce unwanted biases and not accurately reflect the expected accuracy in real-world scenarios.