“Image, Tell me your story!” Predicting the original meta-context of visual misinformation (2024)

Abstract: To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistencies between the image and its caption, or by detecting forgeries in the image. However, they neglect a crucial point of the human fact-checking process: identifying the original meta-context of the image. By explaining what is actually true about the image, fact-checkers can better detect misinformation, focus their efforts on check-worthy visual content, engage in counter-messaging before misinformation spreads widely, and make their explanation more convincing. Here, we fill this gap by introducing the task of automated image contextualization. We create 5Pils, a dataset of 1,676 fact-checked images with question-answer pairs about their original meta-context. Annotations are based on the 5 Pillars fact-checking framework. We implement a first baseline that grounds the image in its original meta-context using the content of the image and textual evidence retrieved from the open web. Our experiments show promising results while highlighting several open challenges in retrieval and reasoning.

## introduction the surge of visual misinformation poses a growing threat to our society. in 2023, more than 30% of all fact-checked claims contained images, an increasing portion of which are @xcite . journalists and fact-checkers have developed pipelines for detecting and debunking visual misinformation @xcite @xcite . image contextualization is a key component of these pipelines: it establishes the meta-context of the image by collecting evidence and answer-1 github.com/ukplab/5pils ing a concise set of questions. @xcite proposes a framework, which we refer to as "5 pillars", where information about the provenance (was the image used before?), source (who is the source?), date (when was it taken?), location (where was it taken?), and motivation (why was it taken?) serve as the image meta-context. for example, figure 1 shows an out-of-context image purporting to represent an indian plane evacuating people from kabul in 2021. after conducting a 5 pillars contextualization, fact-checkers established that the image was taken in 2013 in a us plane in the philippines. image contextualization benefits to several other components of the human fact-checking (fc) pipeline. (1) it helps identify inconsistencies between false claims and images, as shown in figure 1 . (2) it is a mandatory component of writing a solid debunking article. indeed, debunking requires not only stating why a claim is false but also what alternative is true instead @xcite . (3) it allows writing prebunking articles @xcite , by informing about the image's true meta-context before misinformation spreads widely. (4) it helps assess whether the image is check-worthy. for example, images published by an unreliable source, or depicting critical events (war, crises, ...) deserve more pre-bunking or debunking efforts. image contextualization is a time-consuming task that involves finding several evidence pieces, and combining various tools that require expertise @xcite . despite its crucial position in the human fc pipeline, the automation of image contextualization has been ignored in favor of other components, predominantly manipulation @xcite @xcite and image-caption inconsistency detection @xcite @xcite . the output of these tasks is a score, indicating whether or not the content is misinformation. fact-checkers have expressed skepticism about the real-world performance of these scores, with the risk of publishing incorrect automated verdicts @xcite . in contrast, there is a real interest in methods that provide evidence and additional context @xcite @xcite , which includes image contextualization. the contributions of this work are as follows: (1) we fill the gap by introducing the novel task of automated image contextualization. given an image, a model must identify its meta-context using the image content and evidence from the open web. ## related work visual misinformation existing work on visual misinformation focuses on assigning a veracity score to images. many real-world @xcite @xcite and synthetic @xcite @xcite @xcite datasets have been introduced. separate methods are used for out-ofcontext images, where a score is assigned based on image-caption inconsistencies @xcite @xcite , and manipulated and fake images, where forgeries are detected in the image @xcite @xcite . some works rely on external evidence @xcite @xcite @xcite @xcite @xcite , while others use only the image and the caption @xcite @xcite . @xcite provides a comprehensive survey of the field. unlike these works, we use the image and evidence not to provide a veracity score, but to ground the image in its original meta-context. ## the 5 pillars of image verification the 5 pillars of image verification is a framework proposed by @xcite , which summarizes years of insights on journalistic best practices in image verification @xcite @xcite . it divides image contextualization into 5 components, as shown in figure 1 : the provenance, the source, the date, the location, and the motivation. provenance answers the question "was the image used before?" @xcite . identifying prior uses of the image is essential for answering the subsequent questions. hence, it is considered the most important pillar @xcite . to answer this question, fact-checkers rely mainly on reverse image search (ris) engines. these engines take images as input and return (partially) matching images on the internet @xcite . the source is the person who captured or created the image, to be distinguished from the person who shared the visual misinformation online. establishing the source allows to evaluate the credibility of the original image. a combination of social media profiling, interviews, and ris allows fact-checkers to answer this question. the date, i.e., "when was the image taken?", and the location, i.e., "where was the image taken?", position the image in time and space, and highlight any mismatch with the claimed context. the tasks associated with these two pillars are also known as chronolocation and geolocation @xcite . in the simplest case, the image contains the date in exif format. however, most social media platforms remove exif data from uploaded images @xcite . ris results can also help to find the date and the location. if that is not enough, fact-checkers can define keywords to query search engines and retrieve (partially) matching images, a process detailed in appendix b. in other cases, they search for cues in the image, for example, landmarks, road signs, and clothing. additional tools include street imagery, sun angle analysis, and historical weather records @xcite . the motivation, asks the question "why was the image taken?". in most cases, the motivation is to report on an event. however, some sources may have a particular agenda, for example, if they are political activists. in other cases, a fake image used for malicious purposes might originate from the harmless work of an artist. the source's motivation is different from the claimant's intent, which is explored in @xcite . figure 1 shows the 5 pillars analysis of an out-ofcontext image. examples with manipulated and fake images are shown in appendix c. given its popularity and comprehensive documentation (urbani, 2020; @xcite , we use the 5 pillars framework to define qa pairs for automated image contextualization. ## task definition and evaluation the task is to provide answers for each of the 5 pillars using the image content itself and evidence retrieved from the open web. formally, a model has to generate answer a given a question q, an image i, and evidence items e retrieved from the open web w using a retriever r, @xmath0, i, e) , @xmath1. we impose two restrictions on the open web retriever. (1) it cannot return evidence published after the fc article from which we collected the image and annotations, to avoid temporal leaks @xcite . (2) it cannot return fc articles published by other fc organizations. the answer to provenance ("was the image used before?") is either "yes" when (partially) matching images are retrieved, and "no" or "unknown" otherwise, the latter two being difficult to distinguish in practice. the answer is automatically derived from the retrieved results. therefore, the provenance evaluates the ability of retrievers to obtain evidence that contains previous versions of the same image. we define the provenance score as the recall on images with ground truth answer "yes". we do not penalize nor reward retrievers for collecting matching images when the ground truth is "no" or "unknown". source expects free-form answers, evaluated using the rougel @xcite and meteor @xcite metrics. the expected answers for date are actual timestamps. we evaluate the answers using the exact match (em) and ∆, a custom metric ranging from 1 for predictions that are close in time to the ground truth to 0 for those that are far. if the ground truth answer contains more than one date for the image, for example, if it is a composite image, we compute ∆ based on the smallest time difference that can be obtained by matching pairs of predicted and ground truth answers. formally ∆ is defined as in addition to rougel and meteor, we evaluate motivation with bertscore (berts) @xcite to measure the semantic similarity between ground truth and predicted motivation. ## the 5pils dataset the 5pils dataset consists of 1,676 images annotated with 5 pillars answers. we collect metadata for analysis purposes, including the type of image, the verification strategy, and the verification tools. ## experiments 6.1 baseline model to our knowledge, there is no prior method for automated image contextualization. therefore, we introduce a first baseline for the task, illustrated in figure 4 . the baseline is a pipeline based on the best practices of human fact-checkers and the existing afc literature @xcite . ## conclusion in this work, we fill an important gap in the field of visual misinformation by introducing the task of automated image contextualization. we create 5pils, a dataset of 1,676 fact-checked images with meta-context qa pairs based on the 5 pillars framework. we define a first baseline for the task, which combines the image with textual evidence retrieved from the open web. despite achieving promising performance, several challenges remain. directions for future work include better open web retrievers that combine multiple tools, improved answer generation based on multi-step reasoning, and better methods for removing image manipulations.