Images are found everywhere, from advertisements in printed media to the extensive information available over the Internet. However, it is not precisely known how much of this digital content is free of modification; thus it is important to have identification methods. Image Forgery detection techniques aim to identify if the media has any modification without prior information of the source, either identifying the false object or only assessing if the image is authentic or counterfeited. One of the most common alterations done to images is called splicing, which consists in cropping an object or region from an image and attach it to a different image in order to change the information present in the new source. There are several methods that address this attack, by dividing the image into blocks or regions to obtain characteristics in an effort to identify the inconsistencies caused by the attack. This features can be edges, blobs or characteristics in noise changes, illumination changes obtained throughout the image or in sections. In this work, we propose a method that uses a human perception model called Object Saliency Detection, that helps to assess the region where splicing occurs and later determine whether the region is part of the attack. Later, a Superpixel segmentation process helps us to identify the copied object to point out the splicing attack correctly.