Images Of Real-world Scenes Research Articles

Pseudoneglect during object search in naturalistic scenes

Pseudoneglect, that is the tendency to pay more attention to the left side of space, is typically assessed with paper-and-pencil tasks, particularly line bisection. In the present study, we used an everyday task with more complex stimuli. Subjects’ task was to look for pre-specified objects in images of real-world scenes. In half of the scenes, the search object was located on the left side of the image (L-target); in the other half of the scenes, the target was on the right side (R-target). To control for left–right differences in the composition of the scenes, half of the scenes were mirrored horizontally. Eye-movement recordings were used to track the course of pseudoneglect on a millisecond timescale. Subjects’ initial eye movements were biased to the left of the scene, but less so for R-targets than for L-targets, indicating that pseudoneglect was modulated by task demands and scene guidance. We further analyzed how horizontal gaze positions changed over time. When the data for L- and R-targets were pooled, the leftward bias lasted, on average, until the first second of the search process came to an end. Even for right-side targets, the gaze data showed an early left-bias, which was compensated by adjustments in the direction and amplitude of later saccades. Importantly, we found that pseudoneglect affected search efficiency by leading to less efficient scan paths and consequently longer search times for R-targets compared with L-targets. It may therefore be prudent to take spatial asymmetries into account when studying visual search in scenes.

(Retracted) Dynamic scene images-assisted intelligent control method for industrialized feeding through deep vision learning

In a recirculating aquaculture system (RAS), feeding is an important factor affecting the growth of breeding objects. The traditional feeding methods relied on manual experience, which resulted in high labor costs and bait waste. To deal with these challenges, this paper proposes a dynamic scene images-assisted intelligent control method for industrial feeding through deep vision learning. First, a feeding video is processed according to the interframe difference method to obtain the image of the feeding state of the fish. Then, a modified VGG16 model is developed to determine the feeding state of the fish, transform it into a binary classification problem, and calculate the feeding frequency of the fish. After that, residual bait detection is deployed by adapting the YOLOv5 model. The results of the feeding frequency and the residual bait detection are then used to develop an intelligent feeding strategy to improve the growth rate of the fish and the conversion rate of the bait. Experimental tests on real-world scene images showed that the accuracy of identifying the feeding state by the modified VGG16 model reaches 92.4%. Through the verification of the medium-size RAS, compared with the traditional feeding method, the intelligent feeding method significantly saves manpower and reduce 15% of bait waste.

Investigating age-related differences in ability to distinguish between original and manipulated images.

Manipulated images can have serious and persistent ramifications across many domains: They have undermined trust in political campaigns, incited fear and violence, and fostered dangerous global movements. Despite growing concern about the power of manipulated images to influence people's beliefs and behavior, few studies have examined whether people can detect manipulations and the psychological processes underpinning this task. We asked 5,291 older adults, 5,291 middle-aged adults, and 5,291 young adults to detect and locate manipulations within images of real-world scenes. To determine whether a simple intervention could improve people's ability to detect manipulations, some participants viewed a short video which described the five common manipulation techniques used in the present study. Overall, participants demonstrated a limited ability to distinguish between original and manipulated images. Older adults were less accurate in detecting and locating manipulations than younger and middle-aged adults, and the effect of age varied by manipulation type. The video intervention improved performance marginally. Participants were often overconfident in their decisions, despite having limited ability to detect manipulations. Older adults were more likely than younger and middle-aged adults to report checking for shadow/lighting inconsistencies, a strategy that was not associated with improved discriminability, and less likely to report using other strategies (e.g., photometric inconsistencies) that were associated with improved discriminability. Differences in strategy use might help to account for the age differences in accuracy. Further research is needed to advance our understanding of the psychological mechanisms underlying image manipulation detection and the myriad factors that may enhance or impair performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Robust Scale-Aware Stereo Matching Network

Recently, deep convolutional neural networks (CNNs) have emerged as powerful tools for the correspondence problem in stereo matching task. However, the existence of multiscale objects and inevitable ill-conditioned regions, such as textureless regions, in real-world scene images continue to challenge current CNN architectures. In this article, we present a robust scale-aware stereo matching network, which aims to predict multiscale disparity maps and fuse them to achieve a more accurate disparity map. To this end, powerful feature representations are extracted from stereo images and are concatenated into a 4-D feature volume. The feature volume is then fed into a series of connected encoder–decoder cost aggregation structures for the construction of multiscale cost volumes. Following this, we regress multiscale disparity maps from the multiscale cost volumes and feed them into a fusion module to predict final disparity map. However, uncertainty estimations at each scale and complex disparity relationships among neighboring pixels pose a challenge on the disparity fusion. To overcome this challenge, we design a robust learning-based scale-aware disparity map fusion model, which seeks to map multiscale disparity maps onto the ground truth disparity map by leveraging their complementary strengths. Experimental results show that the proposed network is more robust and outperforms recent methods on standard stereo evaluation benchmarks.

Deep-learning-based computer-generated hologram from a stereo image pair.

We propose a deep-learning-based approach to producing computer-generated holograms (CGHs) of real-world scenes. We design an end-to-end convolutional neural network (the Stereo-to-Hologram Network, SHNet) framework that takes a stereo image pair as input and efficiently synthesizes a monochromatic 3D complex hologram as output. The network is able to rapidly and straightforwardly calculate CGHs from the directly recorded images of real-world scenes, eliminating the need for time-consuming intermediate depth recovery and diffraction-based computations. We demonstrate the 3D reconstructions with clear depth cues obtained from the SHNet-based CGHs by both numerical simulations and optical holographic virtual reality display experiments.

Visual search in naturalistic scenes from foveal to peripheral vision: A comparison between dynamic and static displays.

How important foveal, parafoveal, and peripheral vision are depends on the task. For object search and letter search in static images of real-world scenes, peripheral vision is crucial for efficient search guidance, whereas foveal vision is relatively unimportant. Extending this research, we used gaze-contingent Blindspots and Spotlights to investigate visual search in complex dynamic and static naturalistic scenes. In Experiment 1, we used dynamic scenes only, whereas in Experiments 2 and 3, we directly compared dynamic and static scenes. Each scene contained a static, contextually irrelevant target (i.e., a gray annulus). Scene motion was not predictive of target location. For dynamic scenes, the search-time results from all three experiments converge on the novel finding that neither foveal nor central vision was necessary to attain normal search proficiency. Since motion is known to attract attention and gaze, we explored whether guidance to the target was equally efficient in dynamic as compared to static scenes. We found that the very first saccade was guided by motion in the scene. This was not the case for subsequent saccades made during the scanning epoch, representing the actual search process. Thus, effects of task-irrelevant motion were fast-acting and short-lived. Furthermore, when motion was potentially present (Spotlights) or absent (Blindspots) in foveal or central vision only, we observed differences in verification times for dynamic and static scenes (Experiment 2). When using scenes with greater visual complexity and more motion (Experiment 3), however, the differences between dynamic and static scenes were much reduced.

The forest, the trees, or both? Hierarchy and interactions between gist and object processing during perception of real-world scenes

The global-to-local theories of perception assume that the gist of a scene is computed early and automatically, whereas recognition of objects occurs at a later processing stage, requires attentional resources, and is primed by the representation of gist. To test these theoretical predictions, we investigated the processing hierarchy of gist- and object-recognition and their interaction in two experiments (total N = 60). Backward-masked images of real-world scenes were presented for a range of brief durations - between 8 ms and 100 ms, and participants performed either an object or a background classification task, in separate blocks. We report three main findings. First, scenes' backgrounds were generally classified more accurately than foreground objects, but recognition of objects was boosted to the same level as backgrounds by cueing spatial attention to the exact object's location. Second, backgrounds influence objects' recognition, as objects presented within semantically incongruent backgrounds were classified less accurately. Third, objects influence background categorization, as backgrounds comprising incongruent objects were also classified less accurately. Therefore, the first two findings support the global-to-local theories, implying that gists are indeed more readily perceived than objects, probably at an earlier stage. Yet the latter finding that objects also influence gist recognition suggests a more parallel and interactive view of both processes than previously assumed.

Disrupted object-scene semantics boost scene recall but diminish object recall in drawings from memory.

Humans are highly sensitive to the statistical relationships between features and objects within visual scenes. Inconsistent objects within scenes (e.g., a mailbox in a bedroom) instantly jump out to us and are known to catch our attention. However, it is debated whether such semantic inconsistencies result in boosted memory for the scene, impaired memory, or have no influence on memory. Here, we examined the relationship of scene-object consistencies on memory representations measured through drawings made during recall. Participants (N = 30) were eye-tracked while studying 12 real-world scene images with an added object that was either semantically consistent or inconsistent. After a 6-minute distractor task, they drew the scenes from memory while pen movements were tracked electronically. Online scorers (N = 1,725) rated each drawing for diagnosticity, object detail, spatial detail, and memory errors. Inconsistent scenes were recalled more frequently, but contained less object detail. Further, inconsistent objects elicited more errors reflecting looser memory binding (e.g., migration across images). These results point to a dual effect in memory of boosted global (scene) but diminished local (object) information. Finally, we observed that participants fixate longest on inconsistent objects, but these fixations during study were not correlated with recall performance, time, or drawing order. In sum, these results show a nuanced effect of scene inconsistencies on memory detail during recall.

Quantifying aphantasia through drawing: Those without visual imagery show deficits in object but not spatial memory

Congenital aphantasia is a recently characterized variation of experience defined by the inability to form voluntary visual imagery, in individuals who are otherwise high performing. Because of this specific deficit to visual imagery, individuals with aphantasia serve as an ideal group for probing the nature of representations in visual memory, particularly the interplay of object, spatial, and symbolic information. Here, we conducted a large-scale online study of aphantasia and revealed a dissociation in object and spatial content in their memory representations. Sixty-one individuals with aphantasia and matched controls with typical imagery studied real-world scene images, and were asked to draw them from memory, and then later copy them during a matched perceptual condition. Drawings were objectively quantified by 2,795 online scorers for object and spatial details. Aphantasic participants recalled significantly fewer objects than controls, with less color in their drawings, and an increased reliance on verbal scaffolding. However, aphantasic participants showed high spatial accuracy equivalent to controls, and made significantly fewer memory errors. These differences between groups only manifested during recall, with no differences between groups during the matched perceptual condition. This object-specific memory impairment in individuals with aphantasia provides evidence for separate systems in memory that support object versus spatial information. The study also provides an important experimental validation for the existence of aphantasia as a variation in human imagery experience.

The link between detail generation and eye movements when encoding and retrieving complex images

ABSTRACT Examining eye movement patterns when encoding and retrieving visually complex memories is useful to understand the link between visuo-perceptual processes and how associated details are represented within these memories. Here, we used images of real-world scenes (e.g., a couple grocery shopping) to examine how encoding and retrieval eye movements are linked to the details used to describe complex images during these two phases of memory. Given that memories are often elaborated upon during retrieval, we also examined whether eye-movements at retrieval related to details that were the same as those described when encoding the image (reinstated details) as well as details about the image event that were not initially described at encoding (newly generated details). Testing young healthy participants, we found that retrieval eye movements, specifically eye fixation rate, predicted reinstated details, but not newly generated details. This suggests that visuo-perceptual processes are preferentially engaged at retrieval to reactivate perceived information. At encoding, we found a relationship between eye movements and detail generation that changed over time. This relationship was positive early on in the encoding phase but changed to a negative relationship later in the phase, indicating that a unique relationship exists between activating visuo-perceptual processes during early encoding versus late encoding. Overall, our results provide new insights into how visuo-perceptual processes contribute to different components of complex memory.

Spatial misregistration in hyperspectral cameras: lab characterization and impact on data quality in real-world images

Hyperspectral cameras capture images where every pixel contains spectral information of the corresponding small area of the depicted scene. Spatial misregistration—differences in spatial sampling between different spectral channels—is one of the key quality parameters of these cameras, because it may have a large impact on the accuracy of the captured spectra. Spatial misregistration unifies various factors, such as differences in the position of the optical point spread function (PSF) in different spectral channels, differences in PSF size, and differences in PSF shape. Ideally, there should be no difference in spatial sampling across the spectral channels, but in any real camera, all these factors are present to some degree. Our work shows the magnitude of the spectral errors caused by these spatial misregistration factors of different magnitudes and in various combinations when acquiring hyperspectral images of real scenes. The spectral errors are calculated in Virtual Camera software where high-resolution airborne images of real-world scenes and several PSFs of different hyperspectral cameras are used as the input. The misregistration factors are simulated. Two different methods for quantifying spatial misregistration in the lab are also tested and compared using the correlation with the errors in the real-world scenes as the criterion. The results are used to suggest the best camera characterization approach that would adequately predict spatial misregistration errors and allow reliable comparison of different hyperspectral cameras to each other.

Visual-Inertial Odometer-Based Global High Precision Indoor Human Navigation in a University Library

Abstract. AR (Augmented Reality) could be realized as a basic and high-level function on latest smartphones with a reasonable price. AR enables users to experience consistent three-dimensional (3D) spaces co-existing with 3D real and virtual objects with sensing real 3D environments and reconstructing them in the virtual world through a camera. The accuracy of sensing real 3D environments using an AR function, that is, visual-inertial odometer, of a smartphone is extremely higher than one of a GPS receiver on it, and can be less than one centimeter. However, current common AR applications generally focus on “small” real 3D spaces, not large real 3D spaces. In other words, most of the current AR applications are not designed for uses based on a geographic coordinate system.We proposed a global extension of the visual-inertial odometer with an image recognition function of geo-referenced image markers installed in real 3D spaces. Examples of geo-referenced image markers can be generated from analog guide boards existing in the real world. We tested this framework of a global extension of the visual-inertial odometer embedded in a smartphone on the first floor in the central library of Akita University. The geo-referenced image markers such as floor map boards and book categories sign boards were registered in a database of 3D geo-referenced real-world scene images. Our prototype system developed on a smartphone, that is, iPhone XS, Apple Inc., could first recognized a floor map board (Fig. 1), and could determine the 3D precise distance and direction of the smartphone from the central position of the floor map board in a local 3D coordinate space with the origin point as the central positon of the board. Then, the system could convert the relative precise position and the relative direction of the smartphone’s camera in a local coordinate space into a global precise location and orientation of it. A subject was walking the first floor in the building of the library with a world tracking function of the smartphone. The experimental result shows that the error of tracking a real 3D space of a global coordinate system was accumulated, but not bad. The accumulated error was only about 30 centimeters after the subject’s walking about 30 meters (Fig. 2). We are now planning to improve our prototype system in the accuracy of indoor navigation with calibrating the location and orientation of a smartphone based sequential recognitions of multiple referenced scene image markers which have already existed for a general user services of the library before developing this proposed new services. As the conclusion, the experiment’s result of testing our prototype system was impressive, we are now preparing a more practical high-precision LBS which enables a user to be navigated to the exact location of a book of a user’s interest in a bookshelf on a floor with AR and floor map interfaces.

A deep one-shot network for query-based logo retrieval

Logo detection in real-world scene images is an important problem with applications in advertisement and marketing. Existing general-purpose object detection methods require large training data with annotations for every logo class. These methods do not satisfy the incremental demand of logo classes necessary for practical deployment since it is practically impossible to have such annotated data for new unseen logo. In this work, we develop an easy-to-implement query-based logo detection and localization system by employing a one-shot learning technique using off the shelf neural network components. Given an image of a query logo, our model searches for logo within a given target image and predicts the possible location of the logo by estimating a binary segmentation mask. The proposed model consists of a conditional branch and a segmentation branch. The former gives a conditional latent representation of the given query logo which is combined with feature maps of the segmentation branch at multiple scales in order to obtain the matching location of the query logo in a target image. Feature matching between the latent query representation and multi-scale feature maps of segmentation branch using simple concatenation operation followed by 1 × 1 convolution layer makes our model scale-invariant. Despite its simplicity, our query-based logo retrieval framework achieved superior performance in FlickrLogos-32 and TopLogos-10 dataset over different existing baseline methods.

The Influence of Behavioral Relevance on the Processing of Global Scene Properties: An ERP Study

Abstract Recent work studying the temporal dynamics of visual scene processing (Harel et al., 2016) has found that global scene properties (GSPs) modulate the amplitude of early Event-Related Potentials (ERPs). It is still not clear, however, to what extent the processing of these GSPs is influenced by their behavioral relevance, determined by the goals of the observer. To address this question, we investigated how behavioral relevance, operationalized by the task context impacts the electrophysiological responses to GSPs. In a set of two experiments we recorded ERPs while participants viewed images of real-world scenes, varying along two GSPs, naturalness (manmade/natural) and spatial expanse (open/closed). In Experiment 1, very little attention to scene content was required as participants viewed the scenes while performing an orthogonal fixation-cross task. In Experiment 2 participants saw the same scenes but now had to actively categorize them, based either on their naturalness or spatial expense. We found that task context had very little impact on the early ERP responses to the naturalness and spatial expanse of the scenes: P1, N1, and P2 could distinguish between open and closed scenes and between manmade and natural scenes across both experiments. Further, the specific effects of naturalness and spatial expanse on the ERP components were largely unaffected by their relevance for the task. A task effect was found at the N1 and P2 level, but this effect was manifest across all scene dimensions, indicating a general effect rather than an interaction between task context and GSPs. Together, these findings suggest that the extraction of global scene information reflected in the early ERP components is rapid and very little influenced by top-down observer-based goals.

Emotional modulation of the pupil response in psychopathy.

Psychopathy is a form of personality disorder associated with a deficit in emotional processing. However, there is debate whether this deficit applies to all emotions or exists only for negative emotions. The pupil dilates rapidly in response to emotional stimuli, allowing a time-sensitive index of emotional processing. Across 3 experiments using (a) visual images of real-world scenes, (b) auditory sound clips, and (c) videos of dynamic facial expressions, we measured emotional modulation of the pupil response to both negative and positive stimuli. Participants were 82 male mentally disordered offenders. Psychopathy was measured using the Psychopathy Checklist—Revised to produce factor scores of interpersonal–affective traits (Factor 1) and lifestyle–antisocial traits (Factor 2). Participants with high Factor 1 scores showed reduced emotional modulation of the pupil response to negative images and angry faces but not to any of the positive stimuli. These effects only occurred shortly after the emotion was presented (<2,000 ms), suggesting delayed processing of negative affective stimuli in Factor 1 psychopathy. Factor 2 scores were not associated with any changes in pupil response. There were no effects of psychopathy on the pupil response to the affective sound clips. The results support a specific psychopathic deficit in the processing of negative stimuli related to the interpersonal–affective dimension of psychopathy. We argue that pupillometry is a powerful and noninvasive tool to investigate emotional processing in clinical populations.

Ultra-rapid object categorization in real-world scenes with top-down manipulations.

Humans are able to achieve visual object recognition rapidly and effortlessly. Object categorization is commonly believed to be achieved by interaction between bottom-up and top-down cognitive processing. In the ultra-rapid categorization scenario where the stimuli appear briefly and response time is limited, it is assumed that a first sweep of feedforward information is sufficient to discriminate whether or not an object is present in a scene. However, whether and how feedback/top-down processing is involved in such a brief duration remains an open question. To this end, here, we would like to examine how different top-down manipulations, such as category level, category type and real-world size, interact in ultra-rapid categorization. We have constructed a dataset comprising real-world scene images with a built-in measurement of target object display size. Based on this set of images, we have measured ultra-rapid object categorization performance by human subjects. Standard feedforward computational models representing scene features and a state-of-the-art object detection model were employed for auxiliary investigation. The results showed the influences from 1) animacy (animal, vehicle, food), 2) level of abstraction (people, sport), and 3) real-world size (four target size levels) on ultra-rapid categorization processes. This had an impact to support the involvement of top-down processing when rapidly categorizing certain objects, such as sport at a fine grained level. Our work on human vs. model comparisons also shed light on possible collaboration and integration of the two that may be of interest to both experimental and computational vision researches. All the collected images and behavioral data as well as code and models are publicly available at https://osf.io/mqwjz/.

Visual recall memory contains highly detailed and precise object and spatial information

While there has been much research investigating visual recognition memory, little work has examined the specific content of visual free recall memory, despite evidence that these may be two neurally distinct processes. Here, we quantify the capacity and resolution of visual free recall for complex real-world scene images using a drawing task. Participants (N=30) studied 30 real-world scenes (10s each) and after an 11-min digit span distractor task, drew as many images as possible from memory in as much detail as possible. To serve as benchmarks, separate participants made 1) drawings from the scene category names ("lower bound"), reflecting canonical representations of a scene, and 2) drawings created while looking at the image ("upper bound"), reflecting the maximum information that could be drawn. We leveraged online crowd-sourced experiments on Amazon Mechanical Turk to objectively score the content of these 1,782 drawings. These ratings revealed an impressive detail contained in peoples' memories. First, memory drawings were easily matched to their corresponding image, and were nearly as diagnostic as those drawn directly from the image. Second, memory drawings contained 73.9% of the objects in the drawings made directly from the image, and on average, participants recalled 151.3 objects across the experiment with very few false alarms (1.83 objects across the experiment). Third, the spatial arrangement of objects in memory drawings was highly accurate, and almost identical to the original image. Further, computer vision graph-based visual saliency maps significantly predicted which objects would be remembered by participants. Collectively these results suggest that visual recall memory contains diagnostic, detailed, and precise representations of real-world scenes. Meeting abstract presented at VSS 2018

The influence of behavioral relevance on the processing of global scene properties: An ERP study

Recent work studying the temporal dynamics of visual scene processing (Harel et al., 2016) has found that global scene properties (GSPs) modulate the amplitude of early Event-Related Potentials (ERPs). It is still not clear, however, to what extent the processing of these GSPs is influenced by their behavioral relevance, determined by the goals of the observer. To address this question, we investigated how behavioral relevance, operationalized by the task context impacts the electrophysiological responses to GSPs. In a set of two experiments we recorded ERPs while participants viewed images of real-world scenes, varying along two GSPs, naturalness (manmade/natural) and spatial expanse (open/closed). In Experiment 1, very little attention to scene content was required as participants viewed the scenes while performing an orthogonal fixation-cross task. In Experiment 2 participants saw the same scenes but now had to actively categorize them, based either on their naturalness or spatial expense. We found that task context had very little impact on the early ERP responses to the naturalness and spatial expanse of the scenes: P1, N1, and P2 could distinguish between open and closed scenes and between manmade and natural scenes across both experiments. Further, the specific effects of naturalness and spatial expanse on the ERP components were largely unaffected by their relevance for the task. A task effect was found at the N1 and P2 level, but this effect was manifest across all scene dimensions, indicating a general effect rather than an interaction between task context and GSPs. Together, these findings suggest that the extraction of global scene information reflected in the early ERP components is rapid and very little influenced by top-down observer-based goals.

How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models.

Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available.

Can people identify original and manipulated photos of real-world scenes?

Advances in digital technology mean that the creation of visually compelling photographic fakes is growing at an incredible speed. The prevalence of manipulated photos in our everyday lives invites an important, yet largely unanswered, question: Can people detect photo forgeries? Previous research using simple computer-generated stimuli suggests people are poor at detecting geometrical inconsistencies within a scene. We do not know, however, whether such limitations also apply to real-world scenes that contain common properties that the human visual system is attuned to processing. In two experiments we asked people to detect and locate manipulations within images of real-world scenes. Subjects demonstrated a limited ability to detect original and manipulated images. Furthermore, across both experiments, even when subjects correctly detected manipulated images, they were often unable to locate the manipulation. People’s ability to detect manipulated images was positively correlated with the extent of disruption to the underlying structure of the pixels in the photo. We also explored whether manipulation type and individual differences were associated with people’s ability to identify manipulations. Taken together, our findings show, for the first time, that people have poor ability to identify whether a real-world image is original or has been manipulated. The results have implications for professionals working with digital images in legal, media, and other domains.