We also created a new set of photographic stimuli for Experiment 2. Rather than sourcing photos online, the first author captured a unique set of photos on a Nikon D40 camera in RAW format, and prior to any digital editing, converted the files to PNGs.
There are two crucial benefits to using original photos rather than downloading photos from the web. First, by using original photos we could be certain that our images had not been previously manipulated in any way. Second, when digital images are saved, the data are compressed to reduce the file size.
JPEG compression is lossy in that some information is discarded to reduce file size. This information is not generally noticeable to the human eye except at very high compression rates when compression artifacts can occur ; however, the process of converting RAW files to PNGs a lossless format prevented any loss of data in either the original or manipulated images and, again, ensured that our photos were not manipulated in any way before we intentionally manipulated them.
A further 32 subjects were excluded from the analyses because they had missing response time data for at least one response on the detection or location task. As in Experiment 1, subjects did not receive payment for taking part but were given feedback on their performance at the end of the study. We stopped collecting data once we reached responses per photo. The design was similar to that of Experiment 1.
We checked the photos to ensure there were no spatial distortions caused by the lens, such as barrel or pincushion distortion. The photo manipulation process was the same as in Experiment 1. We applied the five manipulation techniques to six different photos to create a total of 30 manipulated photos.
We used the non-manipulated version of these six photos and another four non-manipulated photos to give a total of ten original photos. Thus, the total number of photos was As in Experiment 1, we ran two independent saliency models to check whether our manipulations had influenced the salience of the region where the manipulation had been made.
See Additional file 2 for details of the saliency analyses. Similar to Experiment 1, our manipulations made little difference to the salience of the regions of the image. The procedure was similar to that used in Experiment 1, except for the following two changes. First, subjects were asked to locate the manipulation regardless of their response in the detection task. Second, subjects were asked to click on one of 12, rather than nine, regions on the photo to locate the manipulation.
We increased the number of regions on the grid to ensure that the manipulations in the photos spanned two regions, on average, as per Experiment 1. As in Experiment 1, subjects spent a reasonable amount of time examining the photos. It is possible that asking all subjects to search for evidence of a manipulation—the location task—regardless of their answer in the detection task, prompted a more careful consideration of the scene. In line with this account, subjects in Experiment 2 spent a mean of 14 s longer per photo on the detection task than those in Experiment 1.
Recall that the results from Experiment 1 suggested that subjects found the location task difficult, even when they correctly detected the photo as manipulated.
Yet, we were unable to conclusively say that location was more difficult than detection because we did not have location data for the manipulated photo trials that subjects failed to detect. For the location task, however, there were two differences to Experiment 1. First, subjects were asked to select one of 12, rather than one of nine, image regions.
Second, we used a new image set; thus, the number of regions manipulated for each image and manipulation type changed. Accordingly, we ran a separate Monte Carlo simulation to determine the chance rate of selecting the correct region. This finding suggests that people are better at the more direct task of locating manipulations than the more generic one of detecting if a photo has been manipulated or not. One possibility is that our assumption that each of the 12 image regions has an equal chance of being picked is too simplistic—perhaps certain image regions never get picked e.
To check this possibility, we ran a second chance performance calculation. In Experiment 2, even when subjects did not think that the image had been manipulated, they still attempted to guess the region that had been changed. Therefore, we can use these localization decisions in the original non-manipulated versions of the six critical photos to determine chance performance in the task.
This analysis allows us to calculate chance based on the regions of non-manipulated images that people actually selected when guessing rather than assuming each of the 12 regions has an equal chance of being picked. This finding supports the idea that subjects are better at the more direct task of locating manipulations than detecting whether a photo has been manipulated or not.
On the manipulated photo trials, asking subjects to locate the manipulation regardless of whether they correctly detected it allowed us to segment accuracy in the following ways: i accurately detected and accurately located hereafter, DL , ii accurately detected but not accurately located DnL , iii inaccurately detected but accurately located nDL , or iv inaccurately detected and inaccurately located nDnL.
Intuitively, it seems most practical to consider the more conservative accuracy—DL—as correct, especially in certain contexts, such as the legal domain, where it is crucial to know not only that an image has been manipulated, but precisely what about it is fake. That said, it might be possible to learn from the DnL and nDL cases to try to better understand how people process manipulated images.
The most common outcomes were for subjects to both accurately detect and accurately locate manipulations, or both inaccurately detect and inaccurately locate manipulations. Subjects infrequently managed to detect and locate airbrushing manipulations; in fact it was more likely that subjects made DnL or nDL responses.
Although this fits with our prediction that plausible manipulations would be more difficult to identify than implausible ones, the pattern of results for geometrical inconsistency, shadow inconsistency, and addition or subtraction do not support our prediction.
Subjects made more DL responses on the plausible addition or subtraction manipulation photos than on either of the implausible types, geometrical manipulations and shadow manipulations. Why, then, are subjects performing better than expected by either of the chance measures on the addition or subtraction manipulations and worse than expected on the airbrushing ones?
Mean proportion of manipulated photos accurately detected and accurately located DL , accurately detected, inaccurately located DnL , inaccurately detected, accurately located nDL , and inaccurately detected, inaccurately located nDnL by manipulation type. The dotted horizontal lines on the bars represent chance performance for each manipulation type from the results of the Monte Carlo simulation. Recall that the results from Experiment 1 suggested a relationship between the correct detection and location of image manipulations and the amount of disruption the manipulations had caused to the underlying structure of the pixels.
Yet, the JPEG format of the images used in Experiment 1 created some re-compression noise in the Delta-E measurements between different images; thus, we wanted to test whether the same finding held with the lossless image format used in Experiment 2.
As shown in Fig. These Pearson correlation coefficients are larger than those in Experiment 1 cf. It is possible that the re-compression noise in the JPEG images in Experiment 1 obscured the relationship between Delta-E and detection and localization performance. This finding suggests that Delta-E is a more useful measure for local, discrete changes to an image than it is for global image changes, such as applying a filter.
Of course, the whole point of manipulating images is to fool observers, to make them believe that something fake is in fact true. Therefore, it might not be particularly surprising to learn that people find it difficult to spot high quality image manipulations.
Yet it is surprising to learn that, even though our subjects never saw the same image more than once, this ability might be dependent on the amount of disruption between the original and manipulated image.
Our findings suggest that manipulation type and the technique used to create the manipulation, for instance, cloning or scaling, might be less important than the extent to which the change affects the underlying pixel structure of the image. To test this possibility, we next consider the relationship between the Delta-E values and the proportion of a correct detection and b location responses by the category of manipulation type.
That is, subjects accurately detected and located more of the addition or subtraction manipulations than the geometry, shadow, or airbrushing manipulations. One possibility is that the five categories of manipulation type introduced different amounts of change between the original and manipulated versions of the images.
To check this, we calculated the mean proportion of correct detections, localizations, and Delta-E values for each of the five categories of manipulation type. These results suggest that the differences in detection and localization rates across the five manipulation types are better accounted for by the extent of the physical change to the image caused by the manipulation, rather than the plausibility of that manipulation.
Yet, given that subjects did not have the opportunity to compare the manipulated and original version of the scene, it is not entirely obvious why amount of change predicts accuracy. Our results suggest that the amount of change between the original and manipulated versions of an image is an important factor in explaining the detectability and localization of manipulations.
Next we considered whether any individual factors are associated with improved ability to detect or locate manipulations. As discussed, we were able to use liberal or stringent criteria for our classification of detection and location accuracy on the manipulated image trials. Accordingly, we ran three models: the first two used the liberal classification for accuracy and replicated the models we ran in Experiment 1 , and the other examined the more stringent classification, DL.
As in Experiment 1, for the detection task, we also ran two repeated measures linear regression GEE models to explore the effect of the predictor variables on signal-detection estimates d' and c. We included the same factors used in the GEE models in Experiment 1. The results of the GEE analyses are shown in Table 5. Using the more liberal accuracy classification, that is, both DL and DnL responses for detection, we found that three factors had an effect on likelihood to respond correctly: response time, general beliefs about the prevalence of photo manipulation, and interest in photography.
As in Experiment 1, faster responses were more likely to be correct than slower responses. Also replicating the finding in Experiment 1, those who believe a greater percentage of photos are digitally manipulated were slightly more likely to correctly identify manipulated photos than those who believe a lower percentage of photos are digitally manipulated. Additionally, in Experiment 2, those interested in photography were slightly more likely to identify image manipulations correctly than those who are not interested in photography.
For the location task, using the more liberal accuracy classification, that is, both DL and nDL responses, we found that two factors had an effect on likelihood to respond correctly. Again there was an effect of response time: In the location task, faster responses were more likely to be correct than slower responses.
Also those with an interest in photography were slightly more likely to correctly locate the manipulation within the photo than those without an interest. Next we considered whether any factors affected our more stringent accuracy classification, that is, being correct on both the detection and location tasks DL.
The results revealed an effect for two factors on likelihood to respond correctly. Specifically, there was an effect of response time with shorter response times being associated with greater accuracy. There was also an effect of interest in photography, with those interested more likely to correctly make DL responses than those not interested. Our GEE models in both Experiments 1 and 2 revealed that shorter response times were linked with more correct responses on both tasks.
As in Experiment 1, this association might be explained by several models of perceptual decision making; however, determining which of these models best accounts for our data is beyond the scope of the current paper. Considering the prevalence of manipulated images in the media, on social networking sites, and in other domains, our findings warrant concern about the extent to which people may be frequently fooled in their daily lives.
Furthermore, we did not find any strong evidence to suggest that individual factors, such as having an interest in photography or beliefs about the extent of image manipulation in society, are associated with improved ability to detect or locate manipulations.
Recall that we looked at two categories of manipulations—implausible and plausible—and we predicted that people would perform better on implausible manipulations because these scenes provide additional evidence that people can use to determine if a photo has been manipulated. Yet the story was not so simple. In Experiment 1, subjects correctly detected more of the implausible photo manipulations than the plausible photo manipulations, but in Experiment 2, the opposite was true.
Further, even when subjects correctly identified the implausible photo manipulations, they did not necessarily go on to accurately locate the manipulation. It is clear that people find it difficult to detect and locate manipulations in real-world photos, regardless of whether those manipulations lead to physically plausible or implausible scenes.
Research in the vision science literature may help to account for these findings. We know that people might have a simplified understanding of the physics in our world Cavanagh, ; Mamassian, It is not necessarily the case that people ignore shadows altogether, but rather that the visual system processes shadows rapidly and uses them only as a generic cue.
It follows, then, that when trying to distinguish between real and manipulated images, our subjects do not seem to have capitalized on the evidence in the implausible manipulation photos to determine whether they were authentic.
Although the plausibility of a manipulation might not be so important when it comes to detecting manipulated images, we found that the extent to which the manipulation disrupts the underlying structure of the pixels might be important.
Indeed, we found a positive correlation between the image metric Delta-E we used to measure the difference between our original and manipulated photos and the likelihood that the photo was correctly classified as manipulated. In other words, the manipulations that created the most change in the underlying pixel values of the photo were most likely to be correctly classified as manipulated. Although this might seem intuitive, recall that our subjects never saw the same scene more than once. That is, they never saw the non-manipulated versions of any of the manipulated photos that they were shown; despite this, their ability to detect the manipulated photos was related to the extent of change in the pixels.
In doing this, subjects might have found the manipulated photos with less change, and thus smaller Delta-E values, were more similar to their prior expectations of what the world looks like—resulting in those photos being incorrectly accepted as authentic more often. At the same time, the manipulated photos with more change, and thus larger Delta-E values, may have been more difficult to match to a prior expectation—resulting in these photos more often being correctly identified as manipulated.
It seems that this difference in ease of finding a match to prior knowledge and expectation for the manipulated photo helped subjects to make an accurate decision. A future investigation using a wider range of stimuli where subjects see more than one of each manipulation type might consider whether there is an interaction between Delta-E and manipulation type. We were surprised to find that subjects performed better on the location task than on the detection task.
Although this is an interesting finding, the reason for it is not immediately apparent. One possibility is that these two tasks might encourage subjects to adopt different strategies and that subjects are better at the more direct task of locating manipulations than the generic one of detecting whether a photo has been manipulated or not.
A drawback, however, is that the difficulty of finding or generating a set of suitable images that allowed all of the manipulation types to be applied reduced the total number of photos that could be tested to some degree. Although, ideally, future work might extend the range of images tested, we nonetheless note the close consistency in results that we obtained across the two different and independent image sets used in Experiments 1 and 2.
However, our findings suggest that this is not going to be a straightforward task. That said, our findings do highlight various possibilities that warrant further consideration, such as training people to make better use of the physical laws of the world, varying how long people have to judge the veracity of a photo, and encouraging a more careful and considered approach to detecting manipulations.
What our findings have shown is that a more careful search of a scene, at the very least, may encourage people to be skeptical about the veracity of photos.
Of course, increased skepticism is not perfect because it comes with an associated cost: a loss of faith in authentic photos. But what should we be skeptical about? Are some changes acceptable and others not? Should the context of the manipulation be taken into account? Though we are unable to answer these complex questions here, we can offer some points for thought.
Although it is true that all image manipulations are to some extent deceptive, not all manipulations are intentionally deceptive. This distinction is an important one and raises the possibility that people do not set out to detect all image manipulations but instead are primarily concerned about forgeries that have been created with the intention to deceive the viewer.
Of course, people might expect that all images provided as evidence, for instance news images, to have been subjected to rigorous validation processes. It is unlikely, however, that people set themselves the same standard for detecting manipulation in every day contexts. Perhaps more important than being able to identify all instances of manipulation, people are most concerned about the extent to which they can trust the message conveyed from the image.
Although this poses an interesting question, our results suggest that people might struggle to detect image manipulations based on either of these definitions. In the current research, not only did subjects find it difficult to accurately locate the specific aspects of the image that had been altered, they also found it difficult to distinguish original, truthful photos from manipulated, untruthful ones. In light of the findings presented in this paper, it is not surprising that World Press Photo have introduced a computerized photo-verification test to their annual photo contest.
But at the end of the day, this is only a competition. What do our findings mean for other contexts in which an incorrect decision about the veracity of a photo can have devastating consequences? Essentially, our results suggest that guidelines and policies governing the acceptable standards for the use of photos, for example, in legal and media domains, should be updated to reflect the unique challenges of photography in the digital age.
We recommend that this is done soon, and that psychological scientists work together with digital forensic experts and relevant end-users to ensure that such policies are built on sound empirical research. The growing sophistication of photo-editing tools means that nearly anyone can make a convincing forgery. Across two experiments, we found that people have an extremely limited ability to detect and locate manipulations of real-world scenes.
Our results in Experiment 1 offer some support to the suggestion that people are better able to identify physically implausible changes than physically plausible ones. But we did not replicate this finding in Experiment 2; instead, our results indicate that the amount of change is more important than the plausibility of the change when it comes to detecting and localizing manipulations.
Furthermore, we did not find any strong evidence to suggest individual factors are associated with improved ability to detect or locate manipulations. Moreover, our results highlight the need to bring current guidelines and policies governing the acceptable standards for the use of photos into the digital age. In Experiment 2, subjects attempted to localize the manipulation regardless of their response in the detection task.
One limitation of the Delta-E measure is that a global change to an image, for instance adjusting the brightness of the entire image, would result in a high Delta-E value, yet such a change is likely to be difficult to detect.
That said, in our research we are only concerned with local image changes and therefore Delta-E provides a useful measure. This is based on a two-tailed test, given that we would predict that detection rates would increase with the amount of change, we might consider a one-tailed test to be appropriate.
With a one-tailed test, the relationship between Delta-E and the proportion of photos correctly detected as manipulated would be significant at the 0. Amsler, M. Earliest symptoms of diseases of the macula. British Journal of Ophthalmology, 37 , — Barlow, H. Possible principles underlying the transformation of sensory messages. Rosenblith Ed. Google Scholar. Bex, P. In sensitivity to spatial distortion in natural scenes. Journal of Vision, 10 , 1— Article PubMed Google Scholar.
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New York: Routledge. Ehinger, K. Change blindness for cast shadows in natural scenes: Even informative shadow changes are missed. Farid, H. Digital doctoring: how to tell the real from the fake. At this stage, students draw from techniques discussed in class to begin crafting their own visual propaganda focusing on the recent Iraq war.
Divided into groups and randomly assigned a supportive or critical stance on the war, students carefully examine the entire collection of Iraqi War images searching for elements that support their point of view. They are encouraged to paint a persuasive picture of the conflict in a way they find compelling.
The following examples in Figures 8 and 9 represent student products from this 2-hour activity. While several technical questions emerge during the activity, most students quickly pick up the basics and move on to more sophisticated techniques. After sharing their collages with classmates, students discuss how they approached the activity and what they learned about the manipulation of images.
While they clearly enjoy the challenge of manipulating the images, many express a concern regarding the larger implications of widespread photo manipulation in the media. The issue of how much manipulation is too much is not an easy question to answer.
In the end, the power of this activity lies in the realization that images must be viewed as a particular conscious or unconscious view of reality and not objective truth. I believe the importance of technology lies in its ability to leverage constructivist approaches in the teaching of social studies…The chief value of technology lies, therefore, in providing the leverage so urgently needed for moving social studies instruction away from passive, teacher-dominated approaches emphasizing recall and regurgitation toward active student centered forms of learning demanding critical and conceptual thinking from all students at all levels.
It is in this spirit, the authors hope to stimulate a dialogue on using accessible computer skills to explore image manipulation in the social studies classroom to enable students to uncover important ideas about perspective and point of view. The intent of this piece is to recognize the powerful role images play in social studies education and the challenge of sourcing images, particularly in the digital age. Additionally, we hope to catalyze new work in the area in which researchers explore not only different scaffolding strategies for critically viewing images, but also efforts at developing a scope and sequence of how teachers might address this complex challenge.
Most helpful would be studies that compare the effectiveness of different pedagogical approaches engendering these skills. It would also be helpful to explore the similarities and differences in reading video as well as still images.
Burke, P. Eyewitnessing : The uses of images as historical evidence. Considine, D. Visual messages: Integrating imagery into instruction 2nd ed.
Crocco, M. Curry, A. Retrieved October 31, , from the US News. Durhams, S. UW-Madison doctors photo to stress diversity. Hobbs, R. Literacy for the information age. Flood, S. Lapp Eds. Hyerle, D. Visual tools for constructing knowledge. Jowett, G. Propaganda and persuasion. Levstik, L. Doing history 2nd ed. Messaris, P. Visual literacy: Image, mind, and reality. Boulder, CO: Westview Press. Visual aspects of media literacy.
Journal of Communication, 48 1 , National Press Photographers Association. NPPA code of ethics. Werner, W. Reading visual texts. Theory and Research in Social Education, 30 3 , Wineburg, S. Historical problem solving: A study of the cognitive processes used in the evaluation of documentary and pictorial evidence. Manipulated images are created to deceive the audiences and form their understanding on how the media presents everything with perfection.
Since more people have access to technology it [ vague ] creates curiosity in the readers mind when they see an image published in newspapers or magazines. The reader begins to question the ethics of the publication which results in a debate. Photo images were considered as a reliable source and were known as a medium of communication to present the truth to the media. There is a growing body of writings devoted to the ethical use of digital editing in photojournalism.
The photo manipulation industry has often been accused of promoting or inciting a distorted and unrealistic image of self; most specifically in younger people.
The world of glamour photography is one specific industry which has been heavily involved with the use of photo manipulation an obviously concerning element as many people look up to celebrities in search of embodying the 'ideal figure'. Photo manipulation has triggered negative responses from both viewers and celebrities. In April , Britney Spears agreed to release "un-airbrushed images of herself next to the digitally altered ones". Governments are exerting pressure on advertisers, and are starting to ban photos that are too airbrushed and edited.
The growing popularity of image manipulation has raised concern as to whether it allows for unrealistic images to be portrayed to the public. In her article "On Photography" , Susan Sontag discusses the objectivity, or lack thereof, in photography, concluding that "photographs, which fiddle with the scale of the world, themselves get reduced, blown up, cropped, retouched, doctored and tricked out".
With the potential to alter body image, debate continues as to whether manipulated images, particularly those in magazines, contribute to self-esteem issues in both men and women. In today's world, photo manipulation has a positive impact by developing the creativity of one's mind or maybe a negative one by removing the art and beauty of capturing something so magnificent and natural or the way it should be. Photoshopping is a neologism for the digital editing of photos.
Despite this, photoshop is widely used as a verb, both colloquially and academically, to refer to retouching, compositing or splicing , and color balancing carried out in the course of graphic design , commercial publishing , and image editing. In popular culture, the term photoshopping is sometimes associated with montages in the form of visual jokes, such as those published on Fark and in MAD Magazine.
Images may be propagated memetically via e-mail as humor or passed as actual news in a form of hoax. Photomontage of 16 photos which have been digitally manipulated in Photoshop to give the impression that it is a real landscape.
Widely-circulated just after , the "Tourist guy" image is a hoax: the approaching jet was Photoshopped into the World Trade Center roof photo.
Prior to its release to news media, Congressional staff Photoshopped into this official portrait the heads of four members absent from the original photo shoot. Chat WhatsApp.
For more details on the technical processes involved, see Image editing. Focal Press; 4 edition. ISBN The Commissar Vanishes: the falsification of photographs and art in Stalin's Russia. New York: Metropolitan Books. Seventh International Conference of Computer Ethics.
MIT Press. National Press Photographers Association. March 9, The New York Times. Kitchin, Rob The MIT Press. Photo District News. Daily Mail. April 13, Retrieved 23 March Retrieved March 28, The Juice.
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