Essay

Seeing Past the Camera: The elision of illusion and the actual, revisited

September 2026 19 min read

A picture of the Pope in a white puffer jacket was taken for a photograph by a great many people in March 2023, and the mistake has a long history: Leonardo’s buckler, the Lumières’ train, the rendered spaces of games. This essay asks whether anything but the medium has changed, and argues that the process is the same, an audience learning to read a new kind of image for the signs of how it was made. An image generator can produce the signs of any camera or style without the apparatus that once made them, and well enough that looking no longer settles how an image was made. A viewer can therefore no longer trust that any image, a photograph included, was made the way its signs say, and the question has moved from how an image was made to where it came from.

The story of being fooled

On Friday 24 March 2023 an image of Pope Francis in a long white puffer jacket was posted to the Midjourney subreddit, spread on Twitter over the weekend, and by the Monday NBC News was reporting that the pictures “fool the internet”.1 An audience mistaking a new kind of image for the thing it depicts is an old story. Vasari, writing in 1568, tells it of Leonardo’s buckler, painted with a monster that made Leonardo’s father, Ser Piero, start at first sight.2 It is told of the Lumières’ train in 1896, although Stephen Bottomore, in a history of the “train effect”, found that panic was rare and that accounts of it were spread partly “from motives of publicity by some showmen”,3 so that the story circulated whether or not the event had. It recurred with computer-generated images, the subject of my 2013 master’s thesis, in photographs of game spaces where a virtual camera was given the marks of a real one so that a rendered space would read as captured.4 The puffer jacket is one more instance of the story rather than a new kind of event.

Each of these is a case of one fascination, with an image that can pass for what it shows, and the fascination recurs with each new medium rather than belonging to any one of them.5 Leon Gurevitch has already made the move from the “train effect” to a “game effect”.6 The generated image is read here as the next case in that sequence, a continuation of one process in which an audience meets a new kind of image and learns, over time, how to read it. This essay asks whether anything but the medium has changed, and argues that the generated image differs from those before it in ways that are of degree rather than of nature.

How an audience learns to read a new image

In tests I ran in 2012 for my master’s thesis, participants were shown photographs of game spaces mixed with photographs of physical ones and asked which was which.7 They read the images for the qualities of the render: harsh light against the diffused light of a cloudy day, a large depth of field where the photographs had a short one, the look of low resolution and an over-sharpened feel. One participant read the arrangement of the bushes instead, since game developers tend to space objects too evenly when they imitate randomness, and identified the origin of every image.8 Both readings look past what the image shows to how it was made. Socialisation to a new kind of image consists in learning which signs point to which process.

Once an audience knows the signs, a maker can use them, and game developers could, because their audiences “are already socialized to perceive visual media through the lens of the camera”.9 The trust in those signs comes from how the camera makes its image, which André Bazin described as an image of the world “formed automatically, without the creative intervention of man”.10 From this follows the distinction between the constructed image and the captured one. In a painting every mark is intended, whereas in a photograph the photographer chooses the frame and everything within it that was in front of the lens is taken in, intended or not.

The constructed image and the captured one are also true in different ways. A formula attributed to Pierre Bonnard, and best known as the epigraph to Josipovici’s novel about him,11 puts the painter’s way as many little lies told for the sake of one large truth. It describes the constructed image well: no single mark is the subject, and the whole can still be a true depiction because a person directed each departure from appearance. The captured image makes its claim the other way round, through every part of the frame at once, because nothing in it was put there by a hand.

The digital camera kept the film camera’s signs for the person using it, the body, the shutter sound, and a screen as viewfinder on which “the screenshot and the photograph become aligned”,12 so that its users were socialised to it as a camera although its process was not the film camera’s. A phone still plays a shutter sound although nothing in it moves, and a user can meet the simulated sound before the mechanism it imitates, which leaves the sign pointing to a process they may never have encountered. Because the reading is learned, it is set by when the viewer learned it. Tom Gunning’s account of novelty has astonishment that “gradually gives way to an acceptance of the new technology as second nature”,13 a second nature that “may be less stable than we think”,14 and the instability appears to be generational, because a viewer born into the habit never experienced the astonishment.

The camera established less than the trust later placed in it would suggest. A photograph establishes that something was in front of the lens, and does not establish what that something was. Its truth is the reverse of the painter’s and lies in the parts, not in the whole: each thing in the frame was there, and what the whole shows is left to the viewer. The signs of the camera answered one question, whether a lens had been involved, and the first images to carry those signs without one were not generated but computed.

When the apparatus was first removed

To composite computer-generated elements into Jurassic Park in 1993, “computer-generated images had to be degraded; their perfection had to be diluted to match the imperfection of film’s graininess”.15 The signs of a lens had to be put in on purpose, because the image had never been near one. Manovich, writing in the mid-1990s, observed that computer graphics defines realism as the ability to simulate an object so that its image is indistinguishable from its photograph, and that what the field had achieved was “not realism, but only photorealism”.16

In a photograph a lens flare is often a fault, the lens getting between the viewer and the scene, whereas in a game, rendered in real time, it is put there on purpose as the sign that a lens was involved at all. A game’s camera is a construct with no lens, and “complex algorithms are used to mimic the physical camera with, for example, the addition of lens flares and depth of field”,17 together with the sway of a handheld walk. The render was built to be read as captured, and a viewer who knows the signs of the camera can recognise what it is asking of them.

The socialisation of audiences to this image is visible in what players did with it. Screenshots came to serve “much the same function as the photo in physical environments: pointing out events and occurrences, documenting a sight seen”,18 and The Legend of Zelda: Tears of the Kingdom puts a camera in the player’s hands, with a compendium to fill with its pictures.19 The practice has a community and a name, and one of its practitioners describes “exactly the same skills as all the photographers do… depth of field, silhouettes, exposure”.20

The question this image put to an audience was image origin, whether a photograph showed a physical space or a virtual one, and in the 2012 tests it was answered correctly 88 per cent of the time.21 The two images participants found hardest were both interiors from the virtual space, an office and an airport.22 The question could be settled by looking, imperfectly, and on the render’s signs.

What generation does differently

The opening of The Shining is filmed from the air, and Kubrick’s account of it shows what such a shot once implied: he “hired Greg McGillivray, who is noted for his helicopter work, and he spent several weeks filming”.23 For an audience in 1980 the shot implied a helicopter, a specialist, weeks of flying and a studio able to pay for them. The same shot cost little more than a lens once the first DJI Phantom with a camera built in was announced in October 2013,24 and a viewer who has attributed aerial footage to drones for as long as they have watched anything receives the framing without the prestige. An image generator needs no aircraft at all, and it can produce the shot because its corpus contains the images of everyone who did fly.

Hito Steyerl, writing in New Left Review, calls what a generator makes a mean image: “statistical renderings, rather than images of actually existing objects”, which “replace likenesses with likelinesses”.25 The generators this essay concerns are diffusion models, trained to turn noise back into images26 on billions of captioned images gathered from the web, 5.85 billion pairs in one open set.27 Given a prompt, a model starts from noise and removes it until what remains is the kind of image the corpus made probable for those words, and no camera, virtual or otherwise, is pointed at anything. The lens flare the game engine had to add is here neither added nor absent: it is in the corpus as a property of photographs in general, not as a decision about this one, and brushwork, halftone and film grain are there on the same terms.

Four differences from the render follow from the corpus, and the first is coverage: a game engine imitated one apparatus, the camera, whereas a generator produces the signs of any apparatus and any aesthetic its corpus contains. The second is accessibility, and it has a precedent: the camera allowed “the rendering of naturalistic images by people who were unskilled in the art of painting”,28 rendering then demanded expertise again, and generation demands a sentence. It became a consumer medium between mid-July and late August 2022, as Midjourney opened its beta, DALL-E 2 widened its beta toward a million people on its waitlist and Stable Diffusion was released.29 The third is scale, which concerns how the reading has to be done rather than how the image is made. The first Cottingley fairy photographs were taken in 191730 by two girls, Elsie Wright and Frances Griffiths, and Arthur Conan Doyle defended them as genuine after their two negatives had been examined by experts at Kodak and enlarged on a lantern screen before a sceptical operator, who was “entirely converted”.31 An examination of that kind is not available for each image at the rate images now arrive, so the reading has to be constant where it was once occasional. The fourth is quality, and the output is now good enough that the cues which would prompt the reading at all are going.

Real or generated

The question audiences now put to images has a community named for it: a subreddit called r/RealOrAI asks of each image posted whether it is a photograph or a generation, and Elmas has studied a year of its activity, more than ten thousand comments, for how its members decide.32 The question is the one the 2012 tests asked, with the contrast term moved. It is no longer whether a photograph showed a physical space or a virtual one but whether the image was made by any apparatus at all, and the renders that were once the other side of the question now count as real. In April 2023 the question was put to an institution, when Boris Eldagsen entered a DALL-E 2 image in the Sony World Photography Awards and won the Creative category of the Open competition. He refused the prize on the grounds that “AI images and photography should not compete with each other” and “AI is not photography”,33 and later proposed promptography as the name for what he had made.

In one study of generated faces, the participants who made the most errors were the most confident, and “White AI faces are judged as human more often than actual human faces”.34 On faces the eye appears to have lost: Nightingale and Farid concluded that synthesis engines “have passed through the uncanny valley”.35 Scenes are less settled, and participants in one study described the newer generated landscapes and interiors as “too perfect”,36 a phrase Manovich used of computer graphics thirty years earlier.37 None of these results is comparable with the 88 per cent of 2012, which tested different images with different people on a different question, so the comparison that matters is in the cues. Kamali et al. sort the cues that give away a generated image into five categories, “anatomical, stylistic, functional, violations of physics, and sociocultural”,38 and only the stylistic one concerns how the image is rendered, while the rest concern whether the scene could be coherent. The participant who studied the bushes in 2012 read the images in the same way.

The cues also move with the models: Högemann et al. found 63.7 per cent accuracy overall and 29 per cent on the newest model they tested.39 Training helps within that limit: tips on technique improved detection40 and five short interventions raised discernment “by up to 13 percentage points”,41 a real gain made of cues the next model may not leave in place. Golby, who wrote about the papal image in a Guardian column, checked its hands because, as he put it, “AI struggles with hands”,42 and by 2024 the hand is the kind of cue Kamali et al. file under anatomical.43

Two things can happen when an image passes as a photograph and is not one. The early film audiences Gunning describes were “sophisticated urban pleasure seekers”44 and what they came for was “a pleasurable vacillation between belief and doubt”,45 and the puffer jacket was posted to a community that makes such images in order to admire what the machine had managed. When the same image crossed to a feed with no frame around it, a viewer took it for a photograph. Nothing in the pixels had changed, only the context, which moved the viewer from participant to victim. A viewer is fooled rather than entertained only when two conditions hold, that the image passes and that the context fails to say, and the studies above measure only the first. Golby’s column appeared three days after the image and began “It happened to me”.46 He had pinched and zoomed and posted it anyway, and he records that the tools “seems to have leapt forwards” since “last year”. Earlier media required “a slow and ongoing audiovisual socialization”;47 this one ran in public and within months.

The doubt also runs the other way, from the generated image to the genuine one. Writing in the California Law Review, Chesney and Citron named the liar’s dividend, by which a public that knows fakes exist can be told that a genuine recording is one, and the dividend “flows, perversely, in proportion to success in educating the public about the dangers of deep fakes”.48 On their account every real recording becomes deniable, and the better the audience is socialised the more deniable it is. The evidence on how well the tactic works is unsettled, and two recent studies disagree about whether it works against video. Schiff, Schiff and Bueno, across five experiments with more than 15,000 participants, found that such claims “prove effective against text-based reports but largely fail against video evidence”,49 while Grohmann, Halle and Appel found a false claim that a real video was a deepfake raising a politician’s perceived leadership.50 If a real photograph can be taken for a generation, the doubt no longer attaches to one kind of image at all.

Every image at once

Google’s Pixel 8 offers a feature called Best Take, which, as TechCrunch described it, “combines multiple group photos to create a version where anyone is not blinking or looking away”, so that a user can “choose expressions of different people from various shots and merge them into one”.51 The result is a photograph of a moment that did not occur, made by the camera rather than against it, and an ordinary group picture may now be one. The doubt reaches back into the corpus as well, which was gathered from the web. The aerial images a generator draws on for the helicopter shot were made by people who flew, and once any such image can be generated, the ones that were flown for are open to the same question as the ones that were not. The doubt attaches, then, not to a new class of image but to images in general.

The strongest case against that conclusion is made for one class in particular. Bátori argues, in a 2026 paper in Synthese, that images generated from a user’s own photographs “possess photographic indexicality in their constituent parts… while lacking indexicality as wholes”, that they are comparable to photomontage, and that they belong to photography because the user keeps “meaningful human control” over conception, prompting, iteration and selection.52 The argument is made for images prompted with photographs rather than for generation from text, and within that scope his account of the parts is exact. The control he describes is real, but it is control over the request and the choice among outputs, not over what the image shows, whose particulars come from the corpus. Set beside the two kinds of truth described earlier, such an image has the photograph’s truth in its parts and lacks the painter’s in its whole, because no one directed each departure from appearance.

The Cottingley negatives passed their examinations, and the examinations were right about them, because they were single exposures and unaltered. Doyle took the negatives to the Kodak offices, where two of the company’s experts “could find any evidence of superposition, or other trick” in neither, although they “would not undertake to say that these were preternatural”,53 and the photographic expert Snelling reported them “entirely genuine, unfaked photographs of single exposure, open-air work” with “no trace whatever of studio work involving card or paper models”.54 Snelling was right about the plates and wrong about what was in front of the lens, because the fairies were paper cut-outs.55 The examinations could answer the camera’s question, whether something had been in front of the lens, but not the viewer’s. In the thesis’s words, “what allowed the fairies to be viewed as real wasn’t only the quality of the render, but also the circumstances surrounding the production of the images and audience expectations of the technical capabilities of the medium”.56 Belief travelled through expertise and through what the technology was believed capable of, which is to say through provenance, in 1921.

Elmas’s year of r/RealOrAI shows where viewers take the question now: individuals reasoned from perceptual features 70 per cent of the time and from provenance 4 per cent, and the community’s summaries amplified provenance reasoning 4.3 times.57 A viewer alone looks at the image, and viewers together ask where it came from. The institutional form of the question has moved into the camera: Leica’s M11-P is “the world’s first camera with Content Credentials built-in”, and each file records “who captured an image and when, and how they did so”.58 The credential is the Kodak expert built in at capture, an examination the viewer neither performs nor could, and what it asks the viewer to trust is not the image but the maker of the camera and the process that wrote the record.

Rini argues that recordings have served as an “epistemic backstop” to testimony, that the authority of still photographs “has already been eroded by Photoshop”, and that the gravest danger of deepfakes is “not that they will trick us into believing false content, but that they will gradually eliminate the epistemic credentials of all recordings”.59 Habgood-Coote replies that “photographic manipulation history reveals important precedents” and that deepfakes become less concerning once “social norms’ role in recording epistemology” is recognised.60 Provenance was part of the reading at Cottingley and is where the question has gone now, with the 4 per cent as the measure of how far it is from answering it. While that stays unsettled, generated images are already replacing photographs in commercial work where the question was never asked.

What is being replaced

For blog articles and website builds I no longer license stock photographs; I write a prompt and generate the image. There is no fee and no permission to obtain, the picture fits the scenario, and the people in it are nobody, which for a stock image is the intention. None of my clients has asked whether these images were photographs, and this is substitution where photography was already only photorealism, because the stock image was never wanted as a picture of anyone in particular. In The Image Factory, his study of the stock and visual content business, Paul Frosh describes an industry of “formulaic and stereotypical ‘generic’ images” made to “tolerate multiple reuse”,61 and a stock photograph of a woman laughing at a salad is the look of a photograph of no one, which is what was ordered. Getty’s figures are consistent with this, its creative business growing 0.7 per cent across 2025 while editorial, the coverage of events, grew 6.9 per cent,62 so that the picture of something that happened held its value better than the picture of something in general.

Fashion drew the line in public in the same year, when H&M published campaign images made with consented and watermarked “digital twins” of its models.63 A month later Guess ran models that did not exist in Vogue, disclosed in small print, and a reader said the magazine had “lost credibility”.64 The objection was not to generation as such but to being made to look at a person who was not one without being told. A generated image that stands in for a real person or event fails differently from a generated salad, as Amnesty found when it posted generated images of the 2021 Colombian protests and, after criticism from photojournalists, deleted them.65 The rule being applied in each case was one the eye cannot apply, whether the process that made the image was the one claimed.

Where the objection falls can be predicted from what the audience valued in the image before. If an audience reads an image as the work of a person, an image that gives the same reading without the work gives the value of the reading without the value of the maker, a loss that is felt as trickery where it is felt at all. Nobody mourned the maker of a stock photograph, and nobody objected when the drone took the helicopter’s shot for the price of a lens, because the value of those images had never been in who made them. The Vogue reader objected to being shown a person who was not one, and the photojournalists objected to a record of an event that no witness had made, because in those images the maker’s presence was the value. On the distinction between the constructed image and the captured one, the generated image belongs on the constructed side although no hand is involved, because its accidents are the model’s, not the world’s, and not the maker’s either, as stock already was in purpose. Photography remains where the image is needed as a record, because being captured is what the record consists of.

There is nothing improper in the substitution as far as it goes, and the same tools can present a generated image as a record, which is where the old problem returns under new terms. To the question the essay opened with, whether anything but the medium has changed, the answer is that the process has not. An audience has met a new kind of image and is learning, in public and quickly, to read it, as audiences did for the buckler, the train and the render, with the change in how far the reading now has to go. The question used to be whether an image was a painting or a photograph, then whether it showed a physical space or a virtual one, and it is now whether the image was generated at all, which is asked of every image and cannot be answered from any of them. The audience that learned to see through a camera is being asked to see past one, and since looking cannot do that, it has begun, reasonably enough, to ask where the image came from instead.

Notes

  1. Tolentino, “AI-generated images of Pope Francis in puffer jacket…” (2023). ↩
  2. Vasari, The lives of the artists (1568/1991), p. 287. ↩
  3. Bottomore, “The panicking audience?” (1999), p. 201. ↩
  4. Paterson, Gaming and photography (2013). ↩
  5. Paterson, Gaming and photography (2013), pp. 9, 12. ↩
  6. Gurevitch, “The cinemas of interactions” (2010). ↩
  7. Paterson, Gaming and photography (2013), pp. 55–56. ↩
  8. Paterson, Gaming and photography (2013), p. 56. ↩
  9. Paterson, Gaming and photography (2013), p. 27. ↩
  10. Bazin, “The ontology of the photographic image (H” (1960), p. 7. ↩
  11. Josipovici, Contre-jour (1986/1998). ↩
  12. Paterson, Gaming and photography (2013), p. 36. ↩
  13. Gunning, “Re-newing old technologies” (2003), p. 40. ↩
  14. Gunning, “Re-newing old technologies” (2003), p. 39. ↩
  15. Manovich, “The paradoxes of digital photography” (1996). ↩
  16. Manovich, “The paradoxes of digital photography” (1996). ↩
  17. Paterson, Gaming and photography (2013), p. 27. ↩
  18. Poremba, “Point and shoot” (2007), p. 50. ↩
  19. Hagues, “Zelda” (2023). ↩
  20. Spies and Urban, “Behind the lens” (2026). ↩
  21. Paterson, Gaming and photography (2013), p. 55. ↩
  22. Paterson, Gaming and photography (2013), p. 55. ↩
  23. Kubrick, quoted in Ciment, “Kubrick on The Shining” (1980). ↩
  24. DJI, DJI released Phantom 2 Vision (2013). ↩
  25. Steyerl, “Mean images” (2023). ↩
  26. Ho et al., Denoising diffusion probabilistic models (2020). Rombach et al., “High-resolution image synthesis with latent diffusion models” (2022). ↩
  27. Schuhmann et al., “LAION-5B” (2022). ↩
  28. Paterson, Gaming and photography (2013), p. 12. ↩
  29. Midjourney, “We're officially moving to open-beta!” (2022). Wiggers, “OpenAI expands access to DALL-E 2, its powerful…” (2022). Lopez, Celebrating one year of Stable Diffusion (2023). ↩
  30. Paterson, Gaming and photography (2013), p. 13. ↩
  31. Doyle, The coming of the fairies (1921), pp. 32–33, 91–92. ↩
  32. Elmas, Humans cannot detect AI-generated media but communities may,… (2026). ↩
  33. Eldagsen, Sony World Photography Awards 2023 (2023). ↩
  34. Miller et al., “AI hyperrealism” (2023). ↩
  35. Nightingale and Farid, “AI-synthesized faces are indistinguishable from real faces and…” (2022). ↩
  36. Högemann et al., “What you see is not what you get…” (2025). ↩
  37. Manovich, “The paradoxes of digital photography” (1996). ↩
  38. Kamali et al., How to distinguish AI-generated images from authentic photographs (2024). ↩
  39. Högemann et al., “What you see is not what you get…” (2025). ↩
  40. Huang and Hu, “"A warning is not enough” (2025). ↩
  41. Geissler et al., “Designing effective digital literacy interventions for boosting deepfake…” (2026). ↩
  42. Golby, “I thought I was immune to being fooled…” (2023). ↩
  43. Kamali et al., How to distinguish AI-generated images from authentic photographs (2024). ↩
  44. Gunning, “An aesthetic of astonishment” (1989/1995), pp. 116–117. ↩
  45. Gunning, “An aesthetic of astonishment” (1989/1995), pp. 116–117. ↩
  46. Golby, “I thought I was immune to being fooled…” (2023). ↩
  47. Paterson, Gaming and photography (2013), p. 15. ↩
  48. Chesney and Citron, “Deep fakes” (2019). ↩
  49. Schiff et al., “The liar's dividend” (2025). ↩
  50. Grohmann et al., “Deepfake!” (2026). ↩
  51. Mehta, “Google announces AI-powered photo editing features for new…” (2023). ↩
  52. Bátori, “The creative ghost in the algorithm” (2026). ↩
  53. Doyle, The coming of the fairies (1921), pp. 32–33. ↩
  54. Doyle, The coming of the fairies (1921), p. 53. ↩
  55. Paterson, Gaming and photography (2013), p. 13. ↩
  56. Paterson, Gaming and photography (2013), p. 13. ↩
  57. Elmas, Humans cannot detect AI-generated media but communities may,… (2026). ↩
  58. Lyon, “Leica launches world's first camera with Content Credentials” (2023). ↩
  59. Rini, “Deepfakes and the epistemic backstop” (2020), pp. 2, 8, 13. ↩
  60. Habgood-Coote, “Deepfakes and the epistemic apocalypse” (2023). ↩
  61. Frosh, The image factory (2003), pp. 5, 41. ↩
  62. Getty Images, Getty Images reports fourth quarter and full year… (2026). ↩
  63. H&M Group, H&M continues its exploration of creativity with AI (2025). Apparel Resources, “H&M unveils first AI-generated digital model twins for…” (2025). ↩
  64. Allison, “Her features are flawless” (2025). ↩
  65. Pontone, “Amnesty International slammed over AI protest images” (2023). ↩

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