# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 1 Introduction  
Fake news, the dissemination of false information via social networks, has become a pervasive issue in today's digital landscape. Such misinformation has the potential to deceive and garner more attention than factual news due to its deliberate crafting to evoke strong reactions [1]. Notably, events like the Higgs Boson exploration in 2012 generated a flurry of false information, with as many as 600 tweets per minute [2]. Of particular concern is the use of manipulated images, which can implicitly lend credibility to false narratives, thereby eroding public trust [3; 4]. While numerous studies delve into identifying manipulation in images, many focus on datasets containing only specific tampering types for example [5; 6], or contain datasets algorithmically generated such as [7]. Other papers use numerous more specific types of manipulation; for example, [PERSON] and [PERSON] [8] mentioned a kind of manipulation to remove parts of an image and replace them with surroundings. Besides, [PERSON] et al. [9] mentioned healing as a manipulation capable of softening features. With the intention of categorizing manipulations in as few types as possible while still addressing all forms of manipulation, this paper adopts the categorization of image manipulations proposed by [10; 11]. Along with copy-move and splicing, this categorization recognizes retouching as a manipulation technique that involves slightly modifying an image content without completely hiding large portions. Those modifications include applying filters, highlights, resizing, inpainting, or rotating parts of an image. There are two general approaches to identifying image manipulation: active and passive. Active techniques, also known as preemptive, aim to preemptively insert structures in an image that can be used to detect any future changes. These structures can be visible to the human eye, like in watermarking, or hidden using steganography techniques. Manipulation can be reliably detected by assessing the integrity of the inserted structure. However, this approach requires that the structure be inserted correctly in a trustworthy image capture before any manipulation, as these methods only enable the detection of modifications post-insertion [12]. Meanwhile, passive methods, also known as blind methods, use only the content already present in an image and do not require any prior action, making them better suited for use in social media. To detect image forgery, passive methods can either analyze artifacts intrinsic to a digital image or search for inconsistencies in the content of an image. According to [PERSON] and [PERSON] [12], the artifacts present in digital images can be divided into three types: acquisition, format, and manipulation. Acquisition artifacts are introduced in the image either by imperfections created by the manufacture of camera sensors called fixed-pattern noise or by programs used by cameras to process sensor data before storage; the introduction of artifacts in this manner usually follows a predictable pattern, and any divergences to it is evidence of manipulation. Format artifacts, introduced in the digital storage of images by algorithms like the jpeg compression, by removing information less relevant to human eyes, areas of the image with artifacts of this type different than expected indicate the image was manipulated. Manipulation artifacts, that get introduced during image manipulation by a program, like the application of a blur filter that alters areas of an image in predictable patterns, the presence of those known artifacts in areas of an image is evidence of manipulation. The other possible approach involves analyzing the content of an image for inconsistencies. Some inconsistencies are duplicated portions of an image, unnaturally sharp edges, shadow inconsistencies, and perspective inconsistencies [13]. Unfortunately, all those methods of detection have their pros and cons. There is no absolute best method capable of detecting all types of manipulation.