forgery traces does not prove the image’s authenticity. The level of expertise of the forger should also be taken into account. In fact, the possible traces of manipulation will not be the same depending on whether the author is a neophyte, a seasoned photographer or a special effects professional. The author can also use so-called anti-forensic techniques aimed at masking traces of manipulation so that they become undetectable by experts; it is up to the expert to know these techniques and their weaknesses.
1.1.4. Current methods and tools of law enforcement
As technologies evolve over time, detection tools must also adapt. Particularly during the transition from film photography to digital images, the authentication methods that were mainly based on semantic analysis of the scene (visual analysis of defects, consistency of shadows and lighting, vanishing points) have been completed through structural and statistical analyses.
To date, available commercial tools are not helpful to the authentication of an image. Most of the time, experts need to design their own tools. This raises the concern of deciding on what ground results from such tools should be accepted as evidence in court. In order to compensate for this lack of objective and precise tools, the police recruits trainees, who participate in national projects (DEFALS challenge funded by the DGA and the French National Research Agency) or international projects (H2020 projects of the European Commission). The objective is to involve university researchers as well as industrialists and practitioners (forensic experts). In addition, experts develop good practice guides such as the “Best Image Authentication Practice Manual” within the framework of the ENFSI2 to standardize and formalize analysis methodologies.
Digital images are an essential medium of communication in today’s world. People need to be able to trust this method of communication. Therefore, it is essential that news agencies, governments and law enforcement maintain and preserve trust in this essential technology.
1.1.5. Outline of this chapter
Our objective is to recognize each step of the production chain of an image. This information can sometimes appear in the data accompanying the image, called EXIF (Exchangeable Image File Format), which also includes information such as the brand and model of the camera and lens, the time and location of the photograph, and its shooting settings. However, this information can be easily modified, and is often automatically deleted by social media for privacy reasons. Therefore, we are interested in the information left by the operations on the image itself rather than in the metadata. Some methods, like the one presented in Huh et al. (2018), offer to check the consistency of the data present in the image with its EXIF metadata.
Knowledge of the image production chain allows for the detection of changes.
A first application is the authentication of the camera model. The processing chain is specific to each device model; so it is possible to determine the device model by identifying the processing chain, as implemented in Gloe (2012) where features are used to classify photographs according to their source device. More recently, Agarwal and Farid (2017) showed that even steps common to many devices, such as JPEG compression, sometimes have implementation differences that allow us to differentiate models from multiple manufacturers, or even models from the same manufacturer.
Another application is the detection of suspicious regions in an image, based on the study of the residue – sometimes called noise – left by the processing chain. This residue is constituted of all the traces left by each operation. While it is often difficult, or even impossible, to distinguish each step in the processing chain individually, it is easier to distinguish two different processing chains as a whole. Using this idea, Cozzolino and Verdoliva proposed to use steganography tools (see Chapter 5 entitled “Steganography: Embedding data into Multimedia Content”) to extract the image residue (Cozzolino et al. 2015b). Treating this residue as a piece of hidden information in the image, an algorithm such as Expectation–Maximization (EM) is then used to classify the different regions of the image. Subsequently, neural networks have shown good performance in extracting the residue automatically (Cozzolino and Verdoliva 2020; Ghosh et al. 2019), or even in carrying out the classification themselves (Zhou et al. 2018).
The outline of this chapter arises from previous considerations. Section 1.2 describes the main operations of the image processing chain.
Section 1.3 is dedicated to the effect each step of the image processing pipeline has on the image’s noise. This section illustrates how and why the fine analysis of noise enables the reverse engineering of the image and leads to the detection of falsified areas because of the discrepancies in the noise model.
We then detail the two main operations that lead to the final coding of the image. Section 1.4 explains how demosaicing traces can be detected and analyzed to detect suspicious areas of an image. Section 1.5 describes JPEG encoding, which is usually the last step in image formation, and the one that leaves the most traces. Similarly to demosaicing, we show how the JPEG encoding of an image can be reverse-engineered to understand its parameters and detect anomalies. The most typical cases are cropping and local manipulations, such as internal or external copy and paste.
Section 1.6 specifically addresses the detection of internal copy-move, a common type of manipulation. Finally, section 1.7 discusses neural-network-based methods, often efficient but at the cost of interpretability.
Figure 1.2. Simplified processing pipeline of an image, from its acquisition by the camera sensor to its storage as a JPEG-compressed image. The left column represents the image as it goes through each step. The right column plots the noise of the image as a function of intensity in all three channels (red, green, blue). Because each step leaves a specific footprint on the noise pattern of the image, analyzing this noise enables us to reverse engineer the pipeline of an image. This in turn enables us to detect regions of an image which were processed differently, and are thus likely to be falsified
1.2. Describing the image processing chain
The main steps in the digital image acquisition process, illustrated in Figure 1.2, will be briefly described in this section. Other very important steps, such as denoising, are beyond the scope of this chapter and will therefore not be covered here.
1.2.1. Raw image acquisition
The first step of acquiring a raw image consists of counting the number of incident photons over the sensor along the exposure time. There are two different technologies used in camera sensors: charge coupled devices (CCDs) and complementary metal-oxide-semiconductors (CMOS). Although their operating principles differ, both can be modeled in a very similar way (Aguerrebere et al. 2013). Both sensors transform incoming light photons into electronic charge, which interacts with detection devices to produce electrons stored in a potential light well. When the latter is full, the pixels become saturated. The final step is to convert the analog voltage measurements into digital quantized values.
1.2.2. Demosaicing
Most cameras cannot see color directly, because each pixel is obtained through a single sensor that can only count the number of photons reaching it in a certain wavelength range. In order to obtain a color image, a color filter array (CFA) is placed in front of