Toward Trusted Pictures to Combat Fake News

Introduction

Fake news, i.e. the forgery of news information, can have dramatic consequences, from personal harm and legal issues to societal unrest, health risks, financial losses, and threats to democratic processes, to the extent that governments are considering new legislation to combat this threat, e.g., here or there.

Manipulated images are a particular potent medium, where an image is taken out-of-context, with its original message altered to suit one agenda. Current state-of-the-art methods rely on image processing techniques, which, as we will show in this article, are unfortunately not sufficient to detect manipulations.

Fortunately, several actors are working on this problem. We will present two such solutions: CAI and Serelay

Before diving into the details, one might ask why an ISP such as IIJ should care about these fake news. Internet actors are already filtering attacks over the network such as spam, viruses, or Denial-of-Service. We believe they also have some responsibility to stop the dissemination of fake news.

The problem of detecting manipulated images

There exists a lot of tools to detect fake images: fake-inversion, fotoforensics, invid plugin, fakeimagedetector, etc. They either rely on pixels analysis via machine learning, image processing techniques, or search for similar images over the Internet to find the origin of an image.

Unfortunately, these tools are not sufficient, as we show with the following
three images:

The first image originates from a WWF contest in 2019. It shows the extent of deforestation in the Amazon forest by comparing the same forest over a ten years period. The second image has been presented on some social medias as an anti-vaccine protest in Germany in 2021. The third image, named “Rhein II”, was taken by Andreas Gursky in 1999. The artist then edited the image to remove unnecessary details to create this simple and abstract composition.

We ran these images on the fakeimagedetector tool, which leverages Error Level Analysis (ELA) to detect pixel manipulations. The results are quite surprising: the first image looks authentic, while the second and third ones are deemed computer generated or modified.

Actually, the first image does not show the same spot over a ten years period: both sides come from the same image. While deforestation is a real problem, such forgeries might make more harm than good and damage the reputation of associations fighting for the climate.

Regarding the second image, it is authentic, but was taken in Chile in 2011during some students protest, not in Germany in 2021. It is easy to reuse an image for one’s own agenda without having access to additional metadata on the image, such as the time when this image was taken or GPS coordinates.

The third image is also authentic, but was indeed slightly post-processed by its author. The point we make in this third example is that the difference between AI-generated and real images is now very thin, with people believing AI-generated images to be real. Without any authenticity record it would be very easy to deceive someone by pretending an image is AI-generated or not.

One may think the Exif metadata could help to detect these manipulations by showing detailed information to the viewer about the situation in which they have been taken (camera model, GPS location, time and date, etc.). Unfortunately, there are several problems. First, there are no mandatory fields. Camera manufacturers can decide what metadata will be (or not) embedded into the file, and users can modify some settings prior to taking an image. Second, this metadata can be modified or removed by any editing software. Third, image file formats are not secure: they do not embed any signature or similar mechanism to prove their authenticity, keep their integrity against unauthorized modifications, or store confidential information that could prove the image authenticity (e.g., the GPS location of an image taken during a conflict).

To summarize, image processing techniques can detect if the pixels of an image have been manipulated (e.g., part of the image have been modified), but cannot detect whether an image and its associated message are misleading the viewer, for example by pretending an image has been taken at a different place or different time. Part of the problem is the lack of any secure metadata (e.g., when and where the image has been taken) in current image formats: it is relatively easy to remove or alter the Exif metadata.

Existing solutions

We now present two existing solutions to build trusted images, that is to say images whose provenance and history can be certified.

Content Authenticity Initiative

The Content Authenticity Initiative (CAI) was co-founded by Adobe, The New York Times and Twitter in 2019. Its goal is to promote an industry standard for provenance metadata. Since its inception, new actors have joined this initiative, in particular the BBC, ARM, Qualcomm, or even Nikon.

CAI proposes an end-to-end system that adds secure metadata to an image at different stages of its creation, when it is taken by the camera and when it is post-processed (see image below). This secure metadata contains additional information regarding the provenance of the image: when it was taken, who took it, who published it, how it was altered, etc.) This additional information is then secured with hashes and digital signatures. When a user views the image, he can then check the authenticity and provenance of the metadata, effectively increasing his trust in the image.

By embedding secure metadata into the image format instead of storing the secure metadata in another place (a server or another file), this system is transparent to users: they do not need to contact a remote server, thus not needing an Internet connection to check for an image authenticity, and they do not need to store or exchange another file alongside the image.

One downside of this approach is the need for a secure camera. If the image is taken by a normal camera, its authenticity cannot be asserted. Fortunately, smartphones and camera manufacturers are working on new devices compatible with CAI, e.g., Leica, Qualcomm, or Nikon.

Serelay

Serelay is a UK company proposing two phone applications to take secure images and an SDK to verify the authenticity of an image. When taking an image, Serelay application performs dozens of checks to make sure the photographer is not trying to spoof the device location or time, by leveraging GPS coordinates, nearby wifi networks, timezone, as well as analyzing the image to detect for example whether the user is taking an image of another (two-dimensional) image instead of a real scene.

The application then extracts and sends hundreds of datapoints from the image to Serelay servers (which amount to 15kB only according to them). By leveraging their SDK, other users can then check the image authenticity. The figure below shows one such example.

Similarly to CAI, users need to take images with a special camera (in this case, Serelay smartphones application). However, Serelay does not embed the secure metadata with the image, but instead relies on its own servers.

Summary

Existing image processing techniques are not sufficient to detect images manipulated to alter their message. This is a problem in the fight against fake news. Fortunately, several solutions are being developed. These solutions aim at building trust into the image from the moment it is taken by the camera, and then associate additional, secure metadata to the image to prove its authenticity.