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Much of the research and discourse on risks from artificial intelligence AI image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers—seemingly motivated by profit or clout, not ideology—are already using AI-generated images to gain significant traction on Facebook. At times, the Facebook Feed is recommending unlabeled AI-generated images to users who neither follow the Pages posting the images nor realize that the images are AI-generated, highlighting the need for improved transparency and provenance standards as AI models proliferate. With the diffusion of new generative AI tools, policymakers, researchers, and the public have expressed concerns about impacts on different facets of society. Existing work has developed taxonomies of misuses and harm Ferrara, ; Weidinger et al. A significant portion of this literature is theoretical or lab-based and focused on political speech, such as impacts on elections, threats to democracy, and shared capacity for sensemaking Seger et al. And yet, even in the realm of the political, the tactics of manipulators have long been previewed by those with a different motivation: making money. Spammers and scammers are often early adopters of new technologies because they stand to profit during the time gap between when technology makes novel, attention-capturing tactics possible and when defenders recognize the dynamics and come up with new policies or interventions to minimize their impact e. While the misuse of text-to-image and image-to-image models in politics is worthy of study, so are deceptive, non-political applications. Understanding misuse can shape risk analysis and mitigations. In this article, we show that images from AI models are already being used by spammers, scammers, and other creators running Facebook Pages and are, at times, achieving viral engagement. Some Pages we studied did not declare a mutual affiliation, but posted on highly similar topics, recycled posts, and co-moderated Facebook Groups or shared links to the same domains. Other Pages were not clearly connected to others in the list but used highly similar captions, identical generated images, or images on similar themes. A number of Pages, for example, posted AI-generated images of log cabins. The posts are not transparent about the use of AI and many users do not seem to realize that they are of synthetic origin Koebler, The Pages we studied may produce direct and indirect negative impact. AI-generated content appears to be a boon for spam and scam actors because the images are easy to generate, often visually sensational, and attract engagement. In terms of indirect consequences, most of the AI-generated images did not include an indication of their synthetic origin. Comments by Facebook users often suggested that they did not recognize the images were fake—congratulating, for example, an AI-generated child for an AI-generated painting. Scam accounts occasionally engaged with credulous commenters on the posts, both in Pages and Groups, at times seeking personal information about them or offering to sell them products that do not exist. The increasing complexity of distinguishing between real and synthetic content online will likely further exacerbate issues with trust in media and information. To grapple with deceptive AI-generated content, interventions could target at least three different stages: 1 reducing the likelihood that deceptive content reaches end users without notice, 2 decreasing the impact of deceptive content that does reach end users, and 3 measuring real-world use and impact of deceptive AI-generated content on social media platforms. We describe several specific mitigations—improved detection methods, education, and impact assessments—that fall under these three stages, respectively, while recognizing that other approaches can also contribute. First, social media companies should invest resources in improving detection of scams as well as AI-generated content. For the latter, collaborations between AI developers, social media platforms, and external researchers may be useful for ensuring that the most robust detection mechanisms are deployed in practice. Platforms should test the effect of different interventions for indicating that content is AI-generated including labeling images they detect, requiring users to proactively label, and rolling out watermarking techniques Bickert, and researchers should investigate whether tech companies are true to the voluntary commitments announced at the Munich Security Conference e. Labeling AI-generated content can decrease deception of social media users, but it could come at a cost if there are high false-positive or false-negative rates. Second, the media, and AI generation tool creators themselves, should help the public understand AI image generation tools in a manner that is digestible and not sensational. This could include Public Service Announcements that teach that AI-generated images can look photorealistic. Such announcements should learn from recent work on inoculation theory e. Third, researchers should contribute to understanding the effects of AI-generated content on broader information landscapes. Although our study focuses specifically on Facebook, other platforms also face challenges related to AI-generated content. Research has investigated the use of deceptive AI-generated profile pictures on Twitter Yang et al. Accumulation of additional studies through incident databases e. Studies of real-world use can inform which specific applications require prioritization for detection and educational efforts described above. Another line of research can examine how labeling content as AI-generated affects perceptions of unlabeled content Jakesch et al. Finding 1: Spammers, scammers, and other creators are posting unlabeled AI-generated images that are gaining high volumes of engagement on Facebook. Many users do not seem to recognize that the images are synthetic. Unlabeled AI-generated images from the Pages we studied amassed a significant number of views and engagements. One way that we discovered Pages deceptively using AI-generated images was by observing repeated caption text across Pages—even Pages that were seemingly unconnected. Oftentimes these posts received comments of praise. We provide examples of other AI-generated images posted by Pages in the dataset with high levels of engagement in Figure 3. While researchers typically can see the number of engagements a post has the sum of reactions, comments, and reshares , they do not have access to the number of views. One of the 10 most viewed posts in Q3 was an unlabeled AI-generated image from a Page that transitioned from a cooking Page to one showing AI-generated images of kitchens Figure 4. Finding 2: The Facebook Feed at times recommends unlabeled AI-generated images to users who do not explicitly follow the Page posting the content. We suspect these high levels of engagement are partially driven by the Facebook recommendation algorithm. After we conducted preliminary research, we began to see an increasing percentage of AI-generated images in our own Feeds, despite not following or liking any of the Pages posting AI-generated images. The algorithm likely expected us to view or engage with AI-generated images because we had clicked on others in the past. Two colleagues who reviewed our work reported that they were shown AI-generated images in their Feed before they even began investigating, and we observed a number of social media users claiming large influxes of AI-generated images in their Facebook Feeds Koebler, c. Finding 3: Scam and spam Pages leveraging large numbers of AI-generated images are using well-known deceptive practices, such as Page theft or repurposing, and exhibit suspicious follower growth. Research into social media influence dynamics has observed that Page growth is a common strategic goal. Obtaining a Page with an existing following provides a ready-made audience that can be monetized. Some of the Facebook Pages we analyzed used tried-and-true tactics along these lines: 50 had changed their names, often from an entirely different subject, and some displayed a massive jump in followers after the name change but prior to new activity that would organically have produced that follower spike. The second post after the name change received more than 32, likes and 17, comments. Figure 6 shows changes in content from band posters to AI-generated content. No idea how it happened, unfortunately, as I was the only admin and my personal profile is still intact. Figure 6 shows the increase in follower growth. Since our analysis, the Page is no longer live on Facebook. Spam Pages largely leveraged the attention they obtained from viewers to drive them to off-Facebook domains, likely in an effort to garner ad revenue. They would post the AI-generated image often using overlapping captions as described in Finding 1, then leave the URL of the domain they wished users to visit in the first comment. For example, a cluster of Pages that posted images of cabins or tiny homes pointed users to a website that purportedly offered instructions on how to build them. An examination of the posting dynamics of several Pages in our data set—those created prior to easily-available generative AI tools—suggested that they both increased their posting volume and also transitioned from posting primarily clickbait links to their domains to posting attention-grabbing AI-generated images see Figure 7. This is potentially due to the perception that the recommendation engine was likely to privilege one content type over another. Scam Pages used images of animals, homes, and captivating designs as well, but often implied that they sold the product. Users that appeared to be fake new accounts, stolen and reversed profile photos engaged with commenters about the potential to purchase the product or obtain more information. These spam and scam behaviors were distinct from other high-posting-frequency Pages that appeared to be capitalizing on AI-generated image content for audience growth, including some that ran political ads which were not demonstrably manipulative. Finding 4: A subset of Facebook users realized that the images were AI-generated and took steps to warn other users. While most comments on the AI-generated images were unrelated to the artificial nature of the images, some users who encountered the images criticized them for manipulative content or suspicious behavior. They talked about the holidays and supporting local businesses. The change in reviews corresponds to a likely change in Page control, as the Page—which included profile pictures of Catonsville Mercantile—transitioned to posting AI-generated content in May In addition to alerting others through reviews, users posted comments on photorealistic images from several Pages in our data set highlighting that the content is AI-generated. These comments occasionally included alert infographics explaining bad behavior on AI Pages writ large, including engaging in nefarious activities like identifying targets for scams. During the time of our investigation, Meta announced its plan to roll out labeling of AI-generated images that it could detect Clegg, A subsequent announcement put the target date for this effort at May Bickert, The label linked to an article from Congo Check highlighting that the image was AI-generated and engaging in comment-bait, or encouraging interaction to artificially increase engagement and reach Watukalusu, The article cited the high number of engagements with the post; it was likely fact-checked because it went viral. Dozens of similar images from other Pages are not labeled, showing the difficulty of scaling fact-checking of AI-generated images if treated individually. We made determinations about whether Pages were using AI-generated images by finding errors in images and periods of highly similar image creation that bore the aesthetic hallmarks of popular image generators. We describe our specific processes below. First, we noticed Pages posting unlabeled AI-generated images often used overlapping themes with heavy repetition in captions. Searching phrases from the captions in CrowdTangle surfaced other Pages that had posted the same content or highly similar captions. Once we surfaced a Page, we had to make a determination about whether content was AI-generated. To make this assessment, we relied on obvious mistakes or unrealistic images as well as on analyzing trends in posts. Just as Picasso had his Blue Period, the Pages would often go through periods: a few dozen snow carvings, a few dozen watermelon carvings, a few dozen wood carvings, a few dozen plates of artistically arranged sushi—each with a highly similar style. In Figure 12, we provide a screenshot of one such Page moving through different periods. Second, once we had identified a Page for inclusion, we investigated adjacent Pages. Third, we noticed Facebook groups of users interested in finding AI-generated images on the platform. These groups often rely on common open-source intelligence techniques, and they provided several leads for our investigation. Fourth, after several days of engaging with material obtained through these searches, we began to observe unlabeled AI-generated images recommended to us on our own Feeds. Searches that returned a high volume of AI-generated images across many different themes—AI-generated homes, rooms, furniture, clothing, animals, babies, people, food, and artwork—resulted in the subsequent algorithmic suggestion of other AI-generated images across additional random themes. Although manual detection methods were sufficient for identifying Pages described above, our identification method has clear limitations regarding representativeness and exhaustiveness. We discovered Pages that formed clusters, relied on copypasta captions, or were recommended in our Feeds. We only included Pages that posted more than 50 AI-generated images. Since we used manual detection methods for identification, our study over-includes Pages that did not take great precautions to weed out erroneous AI-generated images or intersperse them sufficiently with real images. The images we discovered overwhelmingly included English-language captions. Additional research should examine rather than assume whether and how these findings generalize to non-English speaking audiences on Facebook. Our methods are sufficient for documenting an understudied type of misuse and is characteristic of online investigations , but the Pages we studied are not necessarily reflective of how unlabeled AI-generated images are used on Facebook as a whole. Additional academic studies should continue to investigate AI-generated content in different modalities e. Finally, our methods provide limited insight into Page operator motivations. Since we do not operate the Pages ourselves nor did we interview Page operators, we cannot be sure of their aims. In some cases, their aims seemed obvious e. At other times, the posting pattern seemed designed with the proximate goal of audience growth but an unknown ultimate goal. We encourage future research on the motivations of Page operators sharing AI-generated content, user expectations around synthetic media, and longitudinal investigations examining how those evolve. DiResta, R. How spammers and scammers leverage AI-generated images on Facebook for audience growth. Bickert, M. Our approach to labeling AI-generated content and manipulated media. Meta Newsroom. Caufield, M. SIFT the four moves. Clegg, N. Dixon, R. An argument for hybrid AI incident reporting: Lessons learned from other incident reporting systems. Center for Security and Emerging Technology. Ferrara, E. GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models. Journal of Computational Science, 7, — Goldstein, J. How persuasive is AI-generated propaganda? PNAS Nexus , 3 2. This salesperson does not exist: How tactics from political influence operations on social media are deployed for commercial lead generation. Grbic, D. Social engineering with ChatGPT. Heath, A. Facebook is changing its algorithm to take on TikTok, leaked memo reveals. The Verge. Hughes, H. The Macedonian fake news industry and the US election. Jakesch, M. AI-mediated communication: How the perception that profile text was written by AI affects trustworthiness. Association for Computing Machinery. Koebler, J. Facebook is being overrun with stolen, AI-generated images that people think are real. Inside the world of TikTok spammers and the AI tools that enable them. Limbong, A. NPR Morning Edition. Metaxas, P. Web spam, propaganda and trust. Mouton, C. The operational risks of AI in large-scale biological attacks. RAND Corporation. Munich Security Conference. A tech accord to combat deceptive use of AI in elections. Phua, J. Journal of Marketing Communications, 22 5 , — Roozenbeek, J. Psychological inoculation improves resilience against misinformation on social media. Science Advances, 8 Seger, E. Tackling threats to informed decision-making in democratic societies: Promoting epistemic security in a technologically-advanced world. The Alan Turing Institute. Spitale, G. AI model GPT-3 dis informs us better than humans. Science Advances , 9 Subramanian, S. The Macedonian teens who mastered fake news. Walker, C. Merging AI incidents research with political misinformation research: Introducing the political deepfakes incidents database. Watukalusu, H. Congo Check. Weidinger, L. Taxonomy of risks posed by language models. Yang, A. NBC News. Yang, K. Characteristics and prevalence of fake social media profiles with AI-generated faces. We relied exclusively on publicly available data and did not seek IRB approval. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited. CrowdTangle prohibits users from publishing datasets of posts in full. We, therefore, included screenshots in the paper but cannot provide a full dataset of posts and engagements from the Pages. We thank Abhiram Reddy for excellent research assistance. Article Metrics 0. Josh A. When users see AI-generated images on Facebook, are they aware of the synthetic origins? Essay Summary We studied Facebook Pages that posted at least 50 AI-generated images each, classifying them into spam, scam, and other creator categories. Some were coordinated clusters run by the same administrators. As of April , these Pages had a mean follower count of , and a median of 81, These images collectively received hundreds of millions of exposures. Spam Pages adopted clickbait tactics, directing users to off-platform content farms and low-quality domains. We suspect this is because the algorithmic Feed promotes content that is likely to generate engagement. Comments on the images suggest that many users are unaware of their synthetic origin, though a subset of comments include text or infographics alerting others and warning of scams. Viewer misperceptions highlight the importance of labeling and transparency measures moving forward. Some of the Pages in our sample used known deceptive practices, such as Page theft or takeover, and exhibited suspicious follower growth. Download PDF. Cite this Essay DiResta, R. No funding has been received to conduct this research. The authors declare no competing interests. Authors contributed equally to this research.
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In this way, the goal of this work was to evaluate quality parameters of garlic Allium sativum L. Fifty days after planting dap a foliar application was made with these same doses, maintaining a group of control plants for a total of four treatments per clone. Indicators of external and internal quality were determined, as well as physicochemical properties, protein and carbohydrate content in the garlic extract. The changes found in a few indicators were in the sense of increasing quality and dependent on the variety and concentration of the biostimulant. Keywords: chitosan, chitin, garlic, bioproducts. Resumen: Los bioestimulantes se utilizan con la finalidad de incrementar el crecimiento, desarrollo, rendimientos y calidad de los cultivos. Palabras claves: quitosana, quitina, ajo, bioproductos. Sustainable agriculture is one of the major priorities of the Agenda, which contributes to poverty reduction through approaches in favor of the poor population, promoting the empowerment of family farming, women and youth; as well as the value chain, access to markets and social protection systems. The use of varieties of crops adapted to the conditions of the country and the analysis of production, harvesting and post-harvest processes notably help to develop precision agriculture, with a minimum cost, that guarantees the food demand of the world population. The use and study of biostimulants and their effects is a practical alternative that contributes to the development of sustainable agricultural production systems. Biostimulants are defined as any substance or microorganism capable of increasing nutrition efficiency, tolerance to abiotic stress and the quality of crops Du Jardin, According to Drobek et al they are preparations of natural origin that support the pro-ecological cultivation of vegetables and fruits, whose effects can be multifaceted. Garlic Allium sativum L. In Cuba, the yields of this crop are very low, despite the fact that high volumes of mineral fertilizers and pesticides are applied in its production system Pupo et al. Similarly, those related to the modification of the parameters of quality of the agricultural fruit are insufficient. In this sense, the aim of this work was to evaluate the quality parameters of garlic Allium sativum L. At 50 days after planting dap , foliar applications were made with the same doses of the biostimulant, keeping the group of control plants with water, for a total of four treatments for each clone. This liquid biostimulant is obtained by deacetylation of chitin extracted from lobster exoskeleton. Concentrations of 1. Irrigation was carried out on alternate days with a 10 L watering can and the removal of weed plants by manual weeding. The culture was harvested days after planting and 20 bulbs were selected at random from each treatment for the evaluation of external quality parameters. The raw garlic extract from each of the treatments 5 extracts per treatment was obtained by macerating 5 g of the skinless cloves and subsequent filtration through gauze. The caliber of bulb is a classification according to the equatorial diameter of the bulb. The firmness of the bulb was taken as the maximum force that the bulb supports up to the separation of the cloves, and is expressed in kg F. The measurement was made by holding the refractometer in sunlight. After each measurement three replicates per treatment the prism was cleaned and dried. Then it was centrifuged for 1 min and 1 mL of 2,4-dinitrophenylhydrazine 0. Absorbance readings were recorded at nm. The electrical conductivity EC and pH active acidity of the diluted garlic extract were performed by conductimetry and potentiometry respectively, according to the Cuban standard NC The total measured acidity corresponds to the total acid content in the extract and was determined by acid-base volumetry, using NaOH 0. The calculation was made as a function of the molar mass of each acid taking into account the volumetric law and expressed as a percentage Table 2. The determination of the total protein content in the extract was made by the Biuret method. To an aliquot of the diluted extract, 1 mL of distillated water and 8 mL of the Biuret reagent mixture of copper sulfate in basic medium that forms with the peptide bonds a blue complex were added. The dilution was shaken and around 10 min were waited for good color development. The reducing carbohydrate content in the garlic extract was determined on a Rayleigh UV-visible spectrophotometer and glucose standards 0. The data were processed using the Statgraph v 5. Table 3 Values of caliber, firmness and pungency of the garlic plant bulbs Allium sativum L. Means of three repetitions. In none of the clones were obtained bulbs of 5 caliber mm , this result could be justified by the fact that for the 'Criollo' clones the maximum caliber reached is 4. The increase in the diameter of the garlic bulb in plants cultivated with biostimulants has been reported by several authors Balmori et al. Shafeek et al , in the study of the effect of foliar application of humic acid HA in garlic cultivation Chinese cv. The results for bulb firmness are within the range of values reported by Balmori et al , who used dilutions of a humic vermicomposting extract in the cultivation of the 'Criollo-9' clone as a biostimulant. For pungency pyruvic acid content , which corresponds to the concentration of alliin, the precursor substrate and responsible for the smell and taste of garlic Espinoza et al. These results are in correspondence with those of Denre et al , researchers who in the study of the effect of different concentrations , , and ppm of a commercial humic acid on the pungency of the Gargajali variety of garlic, found a significant increase in this indicator as the concentration of the biostimulant increases. However, they are within the range of values reported by Balmori et al who made foliar applications of dilutions of an extract of humic substances in the cultivar 'Criollo-9'. Electrical conductivity is an indicative of the content of total salts, as well as acidic and basic ionizable substances. Hydronium and hydroxyl ions are the ones that most contribution to the conductivity of a solution. The values found in this work are slightly lower than those of Balmori et al , authors who did find significant differences between the dilutions tested of the humic extract and the control treatment. According to this author, pH levels are mainly determined by irrigation, fertilization and ecological conditions. The content of organic acids influences on the taste of food, color, microbial stability and the quality of preservation, while the content of organic compounds such as proteins and carbohydrates, are related to culinary and medicinal properties of garlic Espinoza et al. Increased protein concentration in plants cultivated with biostimulants has been reported by several authors Anjum et al. Fawzy et al report an increase in protein and nitrogen content in bulbs of garlic plants Chinese cv. Another possibility could be the kind of chitosan, authors such as Costales et al point out that the degree of deacetylation and molecular mass of chitosan influence their biological response, information that is not generally found in commercial chitosan-based biostimulants. Polysaccharides are not quantified by this technique because although they have the carbonyl group, it is small in the macromolecule and does not react. Another possible correlation would be from the identification of the carbohydrates present in the garlic extract by techniques such as high-performance liquid chromatography HPLC. Even though integral mechanization of garlic cultivation is not common in Cuba, machinery such as planters or harvesters can save time and field labor. Knowledge of the physical, mechanical and chemical properties of garlic is of great importance for the mechanization of this crop and its commercial destination. The elements described above and the quantification of the chemical compounds in the different varieties of garlic are important factors when selecting the cultivars with the best chemical characteristics for the food or pharmaceutical industry Espinoza et al. Several authors have justified the different values of the indicators evaluated in this study by the origin of the garlic, the type of soil where the planting was carried out, the cultural attentions provided and environmental factors. According to Akan , the effect of the variety is significant in morphological indicators, but not in the case of biochemical indicators, suggesting that this latter behavior can be explained by genetic and environmental conditions. With the use of biostimulants, not only to increase the size of the agricultural fruit is intended, but also to avoid an alteration to the detriment of the already established quality values for the fruit. AKAN, S. Cuba, 91 p. PUPO, F. Res , 4 4 : , , ISSN: ZAKI, H. Received: 20 July Accepted: 04 December Notes The mention of trademarks of specific equipment, instruments or materials is for identification purposes, there being no promotional commitment in relation to them, neither by the authors nor by the publisher. Saturnina Mesa-Rebato , Prof. The authors of this work declare that they have no conflict of interest.
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How spammers and scammers leverage AI-generated images on Facebook for audience growth
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