Among the methods of image and video editing that negatively impact the forensic field are:  
* Copy/move: consisting of copying a part of the image and pasting it over the same image. In this way, a specific area of the image can be hidden (for example, a weapon). * Cut/paste and splicing: consisting of cutting an object from one image and copying it to another image or creating an image with the contents obtained from two different images, respectively. It has the same effect of hiding a specific area of the image as copy/move or even creating a new scene. * Retouching: this method alters certain characteristics of the image, through techniques such as blurring. An object may appear blurry in the edited image, making it difficult to identify. * Colorization: unlike the previous types of manipulation, the original objects in the image are not hidden, blurred or new, but their color intensities are modified. The impact on the forensic video field is that the version of a witness may differ from the tampered evidence, for example, in clothing colors, skin color or vehicle color, among others. It is colorization that has had the greatest boom in recent years. In manual colorization (Figure 1), the color of the image is altered in specific areas using tools such as Photoshop [5; 6]; while in automatic colorization with deep learning, pairs of grayscale and color images are used to train the models that will later allow them to color images and videos that are initially in grayscale [7; 8; 9]. The counterpart of the generation corresponds to the identification of fake image or video. In the forensic field, it is essential to know if an image or video is authentic, to make that content admissible as digital evidence. In the literature there are many proposals about tampered recognition using active techniques such as watermarking [10; 11] and to a lesser extent works based on passive techniques such as deep learning (DL) [12; 13; 14; 15; 16]. The major limitation of DL-based approaches is the need for large image datasets to carry out model training, whose diversity should include different file formats (e.g., JPEG, TIF, BMP), sizes, color depth (24-bit, 8-bit, 1 bit per pixel), and type of manipulation (manual and  
Figure 1: Example of manual colorization through Photoshop software: (**a**) original grayscale image; (**b**) colorized image corresponding to the girl’s dress. Source: CG-1050 dataset [5; 6]. automatic). In addition, in the case of emerging techniques such as colorization, there are few open-access image or video datasets for this purpose. Some hand-crafted approaches such as the Fake Colorized Image Detection (FCID-HIST and FCID-FE) methods, which are based on histograms and feature coding, highlight the problem of generalization, i.e., they have a significant decrease in performance between the results of internal and external validation [17]. Specifically, for the FCID-HIST method, the result of internal validation in terms of HTER (Half Total Error Rate) is 24.45%, and for external validation it is up to 41.85%. It is pointed out that the lower the HTER value, the better. That is, for the FCID-HIST method you have significantly more misclassifications in the case of external validation. A similar behavior occurs with the FCID-FE method. On the other hand, CNN-based architectures such as WISERNet, specifically designed for the recognition of colorized images, also had problems of generalization [14,15]. For example, the HTER value in internal validation is 0.95%, but for external validation it increases significantly to 22.55%. Using a different dataset, internal validation achieved an HTER of 1.1%, and external validation of 16.6%. Considering that the diversity of training data affects the performance of the classifier, this research addresses the problem of generalization. The main contributions of this research are focused on the following topics:  
* A custom architecture and a transfer-learning-based model for the classification of colorized images are proposed.