Artificial intelligence and image editing tools
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Abstract
Introduction: Artificial Intelligence (AI) and digital image editing tools significantly transformed journalism, visual communication, and contemporary design. The paper analyzes how these technologies optimized visual production processes, allowing everything from automatic image restoration to the generation of hyper-realistic content using advanced models such as Generative Adversarial Networks (GANs). Objectives: to analyze the impact of Artificial Intelligence (AI) and digital image editing tools on journalism, visual communication, and contemporary design, evaluating both their contributions to optimization and creativity in visual production and the risks associated with content manipulation, misinformation, and ethical, legal, and educational challenges. with special emphasis on the Ecuadorian context. Methodology: qualitative approach, of an analytical descriptive nature, aimed at understanding and interpreting the impact of Artificial Intelligence (AI) and digital image editing tools on journalism, visual communication, and contemporary design. A documentary and content analysis design was adopted, which enabled the systematic examination of academic, normative, and professional sources related to the application of AI in visual production, image manipulation and disinformation phenomena, such as deepfakes. Results: the text exposes how the democratization of AI-based tools increased the risk of misinformation, by making it difficult to distinguish between authentic images and synthetic content. Faced with this scenario, technical detection methods stand out, such as forensic noise analysis and audiovisual inconsistencies, which seek to preserve the integrity of visual information. Finally, the paper addresses the ethical, legal, and educational implications of using AI in image editing. Conclusions: the need to establish clear ethical guidelines, promote transparency in automated processes and strengthen media literacy is highlighted. In the Ecuadorian context, progressive progress is recognized, although challenges related to the digital divide, regulation, and public trust in AI-generated content remain. General area of study: Communication. Specific area of study: Digital Imaging. Type of study: original.
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Palacios Shinin, F. J., Vaca Cárdenas, A. N., Murillo Pinos, A. S., & Erazo Tapia, C. P. (2026). Artificial intelligence and image editing tools. ConcienciaDigital, 9(1), 65-96. https://doi.org/10.33262/concienciadigital.v9i1.3597
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References
Abdali, S., Vasilescu, M. A. O., & Papalexakis, E. E. (2021). Deepfake representation with multilinear regression. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2108.06702
Alanazi, F., Ushaw, G., & Morgan, G. (2024). Improving detection of deepfakes through facial region analysis in images. Electronics, 13(1), 126. https://doi.org/10.3390/electronics13010126
Ali, W., & Hassoun, M. (2019). Artificial intelligence and automated journalism: contemporary challenges and new opportunities. International Journal of Media Journalism and Mass Communications, 5(1), 40-49. https://doi.org/10.20431/2454-9479.0501004
Amirian, S., Taha, T., Rasheed, K., & Arabnia, H. (2022). Generative adversarial network applications in creating a meta-universe. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2201.09152
Aparicio-Izurieta, V. (2024). Preferences towards artificial intelligence in Ecuadorian university professors. Sapienza International Journal of Interdisciplinary Studies, 5(1), e24009. https://doi.org/10.51798/sijis.v5i1.730
Arya, S., & Sharma, G. (2023). Generative ai images and Indian media industry: an overview of opportunities and challenges. Journal of Communication and Management, 2(04), 271-274. https://doi.org/10.58966/jcm2023249
Barriga-Fray, S. F., Samaniego-López, M. V., Viñan-Carrasco, L. M., & Benítez-Obando, I. F. (2026). Trends and approaches in inclusive graphic design: a systematic literature review. Societies, 16(1), 25. https://doi.org/10.3390/soc16010025
Buryk, R. (2024). Tendencies in the application of artificial intelligence in the processing of photo materials. Věda a Perspektivy, 2(33), 449-459. https://doi.org/10.52058/2695-1592-2024-2(33)-449-459
Calain, P. (2013). Ethics and images of suffering bodies in humanitarian medicine. Social Science & Medicine, 98, 278-285. https://doi.org/10.1016/j.socscimed.2012.06.027
Casteleiro-Pitrez, J. (2024). Generative artificial intelligence image tools among future designers: a usability, user experience, and emotional analysis. Digital, 4(2), 316-332. https://doi.org/10.3390/digital4020016
Chen, J., Shen, Y., Gao, J., Liu, J., & Liu, X. (2018). Language-based image editing with recurrent attentive models [2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8721–8729]. https://doi.org/10.1109/cvpr.2018.00909
Chi, J. (2024). The evolutionary impact of artificial intelligence on contemporary artistic practices. Communications in Humanities Research, 35(1), 52-57. https://doi.org/10.54254/2753-7064/35/20240006
Cruz-Páez, P., Clavijo, M., & Villacrés, C. (2023). Academia and media in Ecuador: A verification and digital literacy alliance against disinformation. Advances in Social Science, Education and Humanities Research, 41–49. https://doi.org/10.2991/978-2-494069-25-1_6
Dávila-Eskola, Ó., Ponce-Cadena, P., Fuertes-Camacás, B., Bustamante-Granda, R., & Marcillo-Perugachi, L. (2025). Artificial intelligence tools for the development of writing skills in English language learners: Revista Ecos de la Academia, 11(22), e1347. https://doi.org/10.53358/s3csnd72
Eleyan, A., & Alboghbaish, E. (2024). Electrocardiogram signals classification using deep-learning-based incorporated convolutional neural network and long short-term memory framework. Computers, 13(2), 55. https://doi.org/10.3390/computers13020055
Fallis, D. (2020). The epistemic threat of deepfakes. Philosophy & Technology, 34(4), 623-643. https://doi.org/10.1007/s13347-020-00419-2
Fareed, M. W., Bou Nassif, A., & Nofal, E. (2024). Exploring the potentials of artificial intelligence image generators for educating the history of architecture. Heritage, 7(3), 1727-1753. https://doi.org/10.3390/heritage7030081
Gupta, A., Joshi, R., & Laban, R. (2022). Detection of tool based edited images from error level analysis and convolutional neural network. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2204.09075
Gutiérrez-Caneda, B. (2023). Ai application in journalism: chatgpt and the uses and risks of an emergent technology. Profesional de la Información, 32(5), 6. https://dialnet.unirioja.es/servlet/articulo?codigo=9156966
Haeussler, M., Schönig, K., Eckert, H., Eschstruth, A., Mianné, J., Renaud, J.-B., Schneider-Maunoury, Shkumatava, A., Teboul, L., Kent, J., Joly, J.-S., & Concordet, J.-P. (2016). Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biology, 17(1), 148. https://doi.org/10.1186/s13059-016-1012-2
Harris-Love, M., Seamon, B., Teixeira, C., & Ismail, C. (2016). Ultrasound estimates of muscle quality in older adults: reliability and comparison of Photoshop and ImageJ for the grayscale analysis of muscle echogenicity. Peerj, 4(e1721). https://doi.org/10.7717/peerj.1721
He, Z., Kan, M., Zhang, J., & Shan, S. (2020). PA-GAN: progressive attention generative adversarial network for facial attribute editing. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2007.05892
Heim, S., & Chan-Olmsted, S. (2023). Consumer trust in ai–human news collaborative continuum: preferences and influencing factors by news production phases. Journalism and Media, 4(3), 946-965. https://doi.org/10.3390/journalmedia4030061
Hermansyah, M., Najib, A., Farida, A., Sacipto, R., & Rintyarna, B. (2023). Artificial intelligence and ethics: building an artificial intelligence system that ensures privacy and social justice. International Journal of Science and Society, 5(1), 154-168. https://doi.org/10.54783/ijsoc.v5i1.644
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L., & Aerts, H. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510. https://doi.org/10.1038/s41568-018-0016-5
Hsu, C., Zhuang, Y., & Lee, C. (2020). Deep fake image detection based on pairwise learning. Applied Sciences, 10(1), 370. https://doi.org/10.3390/app10010370
Kaan Tuysuz, M., & Kılıç, A. (2023). Analyzing the legal and ethical considerations of deepfake technology. Interdisciplinary Studies in Society, Law, and Politics, 2(2), 4-10. https://doi.org/10.61838/kman.isslp.2.2.2
Korshunov, P., & Marcel, S. (2019). Vulnerability assessment and detection of deepfake videos [2019 International Conference on Biometrics (ICB), 1–6. IEEE]. https://doi.org/10.1109/icb45273.2019.8987375
Krajčovič, P. (2024). The impact of artificial intelligence on social media. European Conference on Social Media, 11(1), 103-110. https://doi.org/10.34190/ecsm.11.1.2237
Láb, F., Štefaniková, S., & Topinková, M. (2019). Photojournalism in central Europe: on authenticity and ethics. Środkowoeuropejskie Studia Polityczne i Medioznawcze, (2), 73-89. https://doi.org/10.14746/ssp.2016.2.5
Le, Q., Ladret, P., Nguyen, H., & Caplier, A. (2022). Computational analysis of correlations between image aesthetic and image naturalness in the relation with image quality. Journal of Imaging, 8(6), 166. https://doi.org/10.3390/jimaging8060166
Liao, S. & Ji, X. (2023). A study on the application of generative artificial intelligence technology in image design [Proceedings of the 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023), 338-350]. https://www.researchgate.net/publication/374617255_A_Study_on_the_Application_of_Generative_Artificial_Intelligence_Technology_in_Image_Design
Liu, H., Wei, Z., Dominguez, A., Li, Y., Wang, X., & Qi, L. (2015). CRISPR-ERA: a comprehensive design tool for CRISPR-mediated gene editing, repression and activation. Bioinformatics, 31(22), 3676-3678. https://doi.org/10.1093/bioinformatics/btv423
Liu, M., Wei, Y., Wu, X., Zuo, W., & Zhang, L. (2023). Survey on leveraging pre-trained generative adversarial networks for image editing and restoration. Science China Information Sciences, 66(5). https://doi.org/10.1007/s11432-022-3679-0
Mäenpää, J. (2021). Distributing ethics: filtering images of death at three news photo desks. Journalism, 23(10), 2230-2248. https://doi.org/10.1177/1464884921996308
Mandell, J., Khurana, B., Folio, L., Hyun, H., Smith, S., Dunne, R., & Andriole, K. (2017). Clinical applications of a CT window blending algorithm: radio (relative attenuation-dependent image overlay). Journal of Digital Imaging, 30(3), 358-368. https://doi.org/10.1007/s10278-017-9941-1
Manuvinakurike, R., Bui, T., Chang, W., & Georgila, K. (2018). Conversational image editing: incremental intent identification in a new dialogue task [Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, 284–295]. https://doi.org/10.18653/v1/w18-5033
Maras, M., & Alexandrou, A. (2018). Determining authenticity of video evidence in the age of artificial intelligence and in the wake of deepfake videos. The International Journal of Evidence & Proof, 23(3), 255-262. https://doi.org/10.1177/1365712718807226
Marathe, A., Jain, P., Walambe, R., & Kotecha, K. (2022). Restorex-ai: a contrastive approach towards guiding image restoration via explainable AI systems. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2204.01719
Mareen, H., Bussche, D., Wallendael, G., Verdoliva, L., & Lambert, P. (2023). Training data improvement for image forgery detection using comprint [2023 IEEE International Conference on Consumer Electronics (ICCE), 1-2]. https://doi.org/10.1109/icce56470.2023.10043503
McCloskey, S., & Albright, M. (2019). Detecting GAN-generated imagery using saturation cues [2019 IEEE International Conference on Image Processing (ICIP), 4584–4588]. https://doi.org/10.1109/icip.2019.8803661
Miranda, C.F., Baldessar, M. J., & Barcelos, M. (2023). Transformations in the productive routine of photojournalism: from analogue to visual journalism with artificial intelligence - ai tools. Concilium, 23(21), 242-254. https://www.researchgate.net/publication/375971634_Transformations_in_the_productive_routine_of_photojournalism_from_analogue_to_visual_journalism_with_Artificial_Intelligence_-_AI_tools_Transformacoes_na_rotina_produtiva_do_fotojornalismo_do_jornalis
Mnassri, K., Farahbakhsh, R., & Crespi, N. (2024). Multilingual hate speech detection: a semi-supervised generative adversarial approach. Entropy, 26(4), 344. https://doi.org/10.3390/e26040344
Mohamed, E. A. S., Osman, M. E., & Mohamed, B. A. (2024). The impact of artificial intelligence on social media content. Journal of Social Sciences, 20(1), 12-16. https://doi.org/10.3844/jssp.2024.12.16
Moran, R., & Shaikh, S. (2022). Robots in the news and newsrooms: unpacking meta-journalistic discourse on the use of artificial intelligence in journalism. Digital Journalism, 10(10), 1756-1774. https://doi.org/10.1080/21670811.2022.2085129
Mukta, M., Ahmad, J., Raiaan, M., Islam, S., Azam, S., Ali, M., & Jonkman, M. (2023). An investigation of the effectiveness of deepfake models and tools. Journal of Sensor and Actuator Networks, 12(4), 61. https://doi.org/10.3390/jsan12040061
Mulcahy, L. (2018). Revolting consumers: a revisionist account of the 1925 ban on photography in English and Welsh courts and its implications for debate about who is able to produce, manage and consume images of the trial. International Journal of Law in Context, 14(4), 559-580. https://doi.org/10.1017/s1744552318000241
Oppenlaender, J. (2022). The creativity of text-to-image generation [Proceedings of the 25th International Academic Mindtrek Conference]. https://doi.org/10.1145/3569219.3569352
Peskersoy, C., Tetik, A., Öztürk, V., & Gökay, N. (2014). Spectrophotometric and computerized evaluation of tooth bleaching employing 10 different home-bleaching procedures: in-vitro study. European Journal of Dentistry, 8(4), 538-545. https://doi.org/10.4103/1305-7456.143639
Ping, Q., Wu, B., Ding, W., & Yuan, J. (2019). Fashion-attgan: attribute-aware fashion editing with multi-objective GAN [2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 323–325. IEEE]. https://doi.org/10.1109/cvprw.2019.00044
Punnappurath, A., Zhao, L., Abdelhamed, A., & Brown, M. S. (2024). Advocating pixel-level authentication of camera-captured images. IEEE Access: Practical Innovations, Open Solutions, 12, 45839–45846 https://doi.org/10.1109/access.2024.3381521
Qin, J. (2023). How does text-to-image ai affect indie game designers and artists? Journal of Innovation and Development, 5(3), 107-111. https://doi.org/10.54097/f7of9f8k
Renzi, G., Rinaldi, A., Russo, C., & Tommasino, C. (2023). A storytelling framework based on multimedia knowledge graph using linked open data and deep neural networks. Multimedia Tools and Applications, 82(20), 31625-31639. https://doi.org/10.1007/s11042-023-14398-x
Rubio, L., & Ruiz, M. (2021). Artificial intelligence and journalism: systematic review of scientific production in Web of Science and Scopus (2008-2019). Communication & Society, 159-176. https://doi.org/10.15581/003.34.2.159-176
Saraswati, N. P. R. T. A. K. H., Lastari, N. K. H., & Asnadi, I W. S. W. (2025). Integrating Canva and similar digital design tools in english language teaching: a literature review. Jurnal Penelitian Ilmu Pendidikan Indonesia, 4(1), 8–13. https://doi.org/10.31004/jpion.v4i1.317
Satrinia, D., Firman, R. R., & Fitriati, T. N. (2023). Potensi artificial intelligence dalam dunia kreativitas desain. Journal of Informatics and Communication Technology (Jict), 5(1), 159-168. https://doi.org/10.52661/j_ict.v5i1.164
Stepanov, A. (2024). A brief overview of existing neural network solutions and services for photographers. Journal of Digital Art & Humanities, 5(1), 31-47. https://doi.org/10.33847/2712-8149.5.1_3
Syahputri, R. A., & Nugraha, J. (2024). Student behavior in using artificial intelligence for Canva instant presentation. Journal of Office Administration: Education and Practice, 4(2), 119–134. https://doi.org/10.26740/joaep.v4n2.p119-134
Twomey, J., Ching, D., Aylett, M. P., Quayle, M., Linehan, C., & Murphy, G. (2023). Do deepfake videos undermine our epistemic trust? a thematic analysis of tweets that discuss deepfakes in the Russian invasion of Ukraine. Plos One, 18(10), e0291668. https://doi.org/10.1371/journal.pone.0291668
Vaccari, C., & Chadwick, A. (2020). Deepfakes and disinformation: exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Society, 6(1), 205630512090340. https://doi.org/10.1177/2056305120903408
Wang, H., Ai, L., Xia, Y., Wang, G., Xiong, Z., & Song, X. (2023a). Software‐based screening for efficient sgRNAs in Lactococcus lactis. Journal of the Science of Food and Agriculture, 104(2), 1200-1206. https://doi.org/10.1002/jsfa.12946
Wang, Q. (2024). Creation is not like a box of chocolates: why is the first judgment recognizing copyrightability of ai-generated content wrong? Grur International, 73(8), 772-777. https://doi.org/10.1093/grurint/ikae082
Wang, T., & Chow, K. (2023). Noise based deepfake detection via multi-head relative-interaction. Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14548-14556. https://doi.org/10.1609/aaai.v37i12.26701
Wang, W., Xiao, H., & Fang, Y. (2024). Clothing image attribute editing based on generative adversarial network, with reference to an upper garment. International Journal of Clothing Science and Technology, 36(2), 268-286. https://doi.org/10.1108/ijcst-09-2023-0129
Wang, X., Guo, H., Hu, S., Chang, M.-C., & Lyu, S. (2023b). Gan-generated faces detection: a survey and new perspectives. Frontiers in Artificial Intelligence and Applications, 372. https://doi.org/10.3233/faia230558
Westerlund, M. (2019). The emergence of deepfake technology: a review. Technology Innovation Management Review, 9(11), 39-52. https://www.researchgate.net/publication/337644519_The_Emergence_of_Deepfake_Technology_A_Review
Zanardelli, M., Guerrini, F., Leonardi, R., & Adami, N. (2022). Image forgery detection: a survey of recent deep-learning approaches. Multimedia Tools and Applications, 82(12), 17521-17566. https://doi.org/10.1007/s11042-022-13797-w
Zhou, K. (2022). Ethical challenges and coping strategies of new media network modeling of photojournalistic images. International Journal of Science and Engineering Applications, 11(12), 376-378. https://doi.org/10.7753/ijsea1112.1041
Ziakis, C., & Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: insights from a comprehensive review. Information, 14(12), 664. https://doi.org/10.3390/info14120664
Alanazi, F., Ushaw, G., & Morgan, G. (2024). Improving detection of deepfakes through facial region analysis in images. Electronics, 13(1), 126. https://doi.org/10.3390/electronics13010126
Ali, W., & Hassoun, M. (2019). Artificial intelligence and automated journalism: contemporary challenges and new opportunities. International Journal of Media Journalism and Mass Communications, 5(1), 40-49. https://doi.org/10.20431/2454-9479.0501004
Amirian, S., Taha, T., Rasheed, K., & Arabnia, H. (2022). Generative adversarial network applications in creating a meta-universe. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2201.09152
Aparicio-Izurieta, V. (2024). Preferences towards artificial intelligence in Ecuadorian university professors. Sapienza International Journal of Interdisciplinary Studies, 5(1), e24009. https://doi.org/10.51798/sijis.v5i1.730
Arya, S., & Sharma, G. (2023). Generative ai images and Indian media industry: an overview of opportunities and challenges. Journal of Communication and Management, 2(04), 271-274. https://doi.org/10.58966/jcm2023249
Barriga-Fray, S. F., Samaniego-López, M. V., Viñan-Carrasco, L. M., & Benítez-Obando, I. F. (2026). Trends and approaches in inclusive graphic design: a systematic literature review. Societies, 16(1), 25. https://doi.org/10.3390/soc16010025
Buryk, R. (2024). Tendencies in the application of artificial intelligence in the processing of photo materials. Věda a Perspektivy, 2(33), 449-459. https://doi.org/10.52058/2695-1592-2024-2(33)-449-459
Calain, P. (2013). Ethics and images of suffering bodies in humanitarian medicine. Social Science & Medicine, 98, 278-285. https://doi.org/10.1016/j.socscimed.2012.06.027
Casteleiro-Pitrez, J. (2024). Generative artificial intelligence image tools among future designers: a usability, user experience, and emotional analysis. Digital, 4(2), 316-332. https://doi.org/10.3390/digital4020016
Chen, J., Shen, Y., Gao, J., Liu, J., & Liu, X. (2018). Language-based image editing with recurrent attentive models [2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8721–8729]. https://doi.org/10.1109/cvpr.2018.00909
Chi, J. (2024). The evolutionary impact of artificial intelligence on contemporary artistic practices. Communications in Humanities Research, 35(1), 52-57. https://doi.org/10.54254/2753-7064/35/20240006
Cruz-Páez, P., Clavijo, M., & Villacrés, C. (2023). Academia and media in Ecuador: A verification and digital literacy alliance against disinformation. Advances in Social Science, Education and Humanities Research, 41–49. https://doi.org/10.2991/978-2-494069-25-1_6
Dávila-Eskola, Ó., Ponce-Cadena, P., Fuertes-Camacás, B., Bustamante-Granda, R., & Marcillo-Perugachi, L. (2025). Artificial intelligence tools for the development of writing skills in English language learners: Revista Ecos de la Academia, 11(22), e1347. https://doi.org/10.53358/s3csnd72
Eleyan, A., & Alboghbaish, E. (2024). Electrocardiogram signals classification using deep-learning-based incorporated convolutional neural network and long short-term memory framework. Computers, 13(2), 55. https://doi.org/10.3390/computers13020055
Fallis, D. (2020). The epistemic threat of deepfakes. Philosophy & Technology, 34(4), 623-643. https://doi.org/10.1007/s13347-020-00419-2
Fareed, M. W., Bou Nassif, A., & Nofal, E. (2024). Exploring the potentials of artificial intelligence image generators for educating the history of architecture. Heritage, 7(3), 1727-1753. https://doi.org/10.3390/heritage7030081
Gupta, A., Joshi, R., & Laban, R. (2022). Detection of tool based edited images from error level analysis and convolutional neural network. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2204.09075
Gutiérrez-Caneda, B. (2023). Ai application in journalism: chatgpt and the uses and risks of an emergent technology. Profesional de la Información, 32(5), 6. https://dialnet.unirioja.es/servlet/articulo?codigo=9156966
Haeussler, M., Schönig, K., Eckert, H., Eschstruth, A., Mianné, J., Renaud, J.-B., Schneider-Maunoury, Shkumatava, A., Teboul, L., Kent, J., Joly, J.-S., & Concordet, J.-P. (2016). Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biology, 17(1), 148. https://doi.org/10.1186/s13059-016-1012-2
Harris-Love, M., Seamon, B., Teixeira, C., & Ismail, C. (2016). Ultrasound estimates of muscle quality in older adults: reliability and comparison of Photoshop and ImageJ for the grayscale analysis of muscle echogenicity. Peerj, 4(e1721). https://doi.org/10.7717/peerj.1721
He, Z., Kan, M., Zhang, J., & Shan, S. (2020). PA-GAN: progressive attention generative adversarial network for facial attribute editing. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2007.05892
Heim, S., & Chan-Olmsted, S. (2023). Consumer trust in ai–human news collaborative continuum: preferences and influencing factors by news production phases. Journalism and Media, 4(3), 946-965. https://doi.org/10.3390/journalmedia4030061
Hermansyah, M., Najib, A., Farida, A., Sacipto, R., & Rintyarna, B. (2023). Artificial intelligence and ethics: building an artificial intelligence system that ensures privacy and social justice. International Journal of Science and Society, 5(1), 154-168. https://doi.org/10.54783/ijsoc.v5i1.644
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L., & Aerts, H. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510. https://doi.org/10.1038/s41568-018-0016-5
Hsu, C., Zhuang, Y., & Lee, C. (2020). Deep fake image detection based on pairwise learning. Applied Sciences, 10(1), 370. https://doi.org/10.3390/app10010370
Kaan Tuysuz, M., & Kılıç, A. (2023). Analyzing the legal and ethical considerations of deepfake technology. Interdisciplinary Studies in Society, Law, and Politics, 2(2), 4-10. https://doi.org/10.61838/kman.isslp.2.2.2
Korshunov, P., & Marcel, S. (2019). Vulnerability assessment and detection of deepfake videos [2019 International Conference on Biometrics (ICB), 1–6. IEEE]. https://doi.org/10.1109/icb45273.2019.8987375
Krajčovič, P. (2024). The impact of artificial intelligence on social media. European Conference on Social Media, 11(1), 103-110. https://doi.org/10.34190/ecsm.11.1.2237
Láb, F., Štefaniková, S., & Topinková, M. (2019). Photojournalism in central Europe: on authenticity and ethics. Środkowoeuropejskie Studia Polityczne i Medioznawcze, (2), 73-89. https://doi.org/10.14746/ssp.2016.2.5
Le, Q., Ladret, P., Nguyen, H., & Caplier, A. (2022). Computational analysis of correlations between image aesthetic and image naturalness in the relation with image quality. Journal of Imaging, 8(6), 166. https://doi.org/10.3390/jimaging8060166
Liao, S. & Ji, X. (2023). A study on the application of generative artificial intelligence technology in image design [Proceedings of the 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023), 338-350]. https://www.researchgate.net/publication/374617255_A_Study_on_the_Application_of_Generative_Artificial_Intelligence_Technology_in_Image_Design
Liu, H., Wei, Z., Dominguez, A., Li, Y., Wang, X., & Qi, L. (2015). CRISPR-ERA: a comprehensive design tool for CRISPR-mediated gene editing, repression and activation. Bioinformatics, 31(22), 3676-3678. https://doi.org/10.1093/bioinformatics/btv423
Liu, M., Wei, Y., Wu, X., Zuo, W., & Zhang, L. (2023). Survey on leveraging pre-trained generative adversarial networks for image editing and restoration. Science China Information Sciences, 66(5). https://doi.org/10.1007/s11432-022-3679-0
Mäenpää, J. (2021). Distributing ethics: filtering images of death at three news photo desks. Journalism, 23(10), 2230-2248. https://doi.org/10.1177/1464884921996308
Mandell, J., Khurana, B., Folio, L., Hyun, H., Smith, S., Dunne, R., & Andriole, K. (2017). Clinical applications of a CT window blending algorithm: radio (relative attenuation-dependent image overlay). Journal of Digital Imaging, 30(3), 358-368. https://doi.org/10.1007/s10278-017-9941-1
Manuvinakurike, R., Bui, T., Chang, W., & Georgila, K. (2018). Conversational image editing: incremental intent identification in a new dialogue task [Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, 284–295]. https://doi.org/10.18653/v1/w18-5033
Maras, M., & Alexandrou, A. (2018). Determining authenticity of video evidence in the age of artificial intelligence and in the wake of deepfake videos. The International Journal of Evidence & Proof, 23(3), 255-262. https://doi.org/10.1177/1365712718807226
Marathe, A., Jain, P., Walambe, R., & Kotecha, K. (2022). Restorex-ai: a contrastive approach towards guiding image restoration via explainable AI systems. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2204.01719
Mareen, H., Bussche, D., Wallendael, G., Verdoliva, L., & Lambert, P. (2023). Training data improvement for image forgery detection using comprint [2023 IEEE International Conference on Consumer Electronics (ICCE), 1-2]. https://doi.org/10.1109/icce56470.2023.10043503
McCloskey, S., & Albright, M. (2019). Detecting GAN-generated imagery using saturation cues [2019 IEEE International Conference on Image Processing (ICIP), 4584–4588]. https://doi.org/10.1109/icip.2019.8803661
Miranda, C.F., Baldessar, M. J., & Barcelos, M. (2023). Transformations in the productive routine of photojournalism: from analogue to visual journalism with artificial intelligence - ai tools. Concilium, 23(21), 242-254. https://www.researchgate.net/publication/375971634_Transformations_in_the_productive_routine_of_photojournalism_from_analogue_to_visual_journalism_with_Artificial_Intelligence_-_AI_tools_Transformacoes_na_rotina_produtiva_do_fotojornalismo_do_jornalis
Mnassri, K., Farahbakhsh, R., & Crespi, N. (2024). Multilingual hate speech detection: a semi-supervised generative adversarial approach. Entropy, 26(4), 344. https://doi.org/10.3390/e26040344
Mohamed, E. A. S., Osman, M. E., & Mohamed, B. A. (2024). The impact of artificial intelligence on social media content. Journal of Social Sciences, 20(1), 12-16. https://doi.org/10.3844/jssp.2024.12.16
Moran, R., & Shaikh, S. (2022). Robots in the news and newsrooms: unpacking meta-journalistic discourse on the use of artificial intelligence in journalism. Digital Journalism, 10(10), 1756-1774. https://doi.org/10.1080/21670811.2022.2085129
Mukta, M., Ahmad, J., Raiaan, M., Islam, S., Azam, S., Ali, M., & Jonkman, M. (2023). An investigation of the effectiveness of deepfake models and tools. Journal of Sensor and Actuator Networks, 12(4), 61. https://doi.org/10.3390/jsan12040061
Mulcahy, L. (2018). Revolting consumers: a revisionist account of the 1925 ban on photography in English and Welsh courts and its implications for debate about who is able to produce, manage and consume images of the trial. International Journal of Law in Context, 14(4), 559-580. https://doi.org/10.1017/s1744552318000241
Oppenlaender, J. (2022). The creativity of text-to-image generation [Proceedings of the 25th International Academic Mindtrek Conference]. https://doi.org/10.1145/3569219.3569352
Peskersoy, C., Tetik, A., Öztürk, V., & Gökay, N. (2014). Spectrophotometric and computerized evaluation of tooth bleaching employing 10 different home-bleaching procedures: in-vitro study. European Journal of Dentistry, 8(4), 538-545. https://doi.org/10.4103/1305-7456.143639
Ping, Q., Wu, B., Ding, W., & Yuan, J. (2019). Fashion-attgan: attribute-aware fashion editing with multi-objective GAN [2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 323–325. IEEE]. https://doi.org/10.1109/cvprw.2019.00044
Punnappurath, A., Zhao, L., Abdelhamed, A., & Brown, M. S. (2024). Advocating pixel-level authentication of camera-captured images. IEEE Access: Practical Innovations, Open Solutions, 12, 45839–45846 https://doi.org/10.1109/access.2024.3381521
Qin, J. (2023). How does text-to-image ai affect indie game designers and artists? Journal of Innovation and Development, 5(3), 107-111. https://doi.org/10.54097/f7of9f8k
Renzi, G., Rinaldi, A., Russo, C., & Tommasino, C. (2023). A storytelling framework based on multimedia knowledge graph using linked open data and deep neural networks. Multimedia Tools and Applications, 82(20), 31625-31639. https://doi.org/10.1007/s11042-023-14398-x
Rubio, L., & Ruiz, M. (2021). Artificial intelligence and journalism: systematic review of scientific production in Web of Science and Scopus (2008-2019). Communication & Society, 159-176. https://doi.org/10.15581/003.34.2.159-176
Saraswati, N. P. R. T. A. K. H., Lastari, N. K. H., & Asnadi, I W. S. W. (2025). Integrating Canva and similar digital design tools in english language teaching: a literature review. Jurnal Penelitian Ilmu Pendidikan Indonesia, 4(1), 8–13. https://doi.org/10.31004/jpion.v4i1.317
Satrinia, D., Firman, R. R., & Fitriati, T. N. (2023). Potensi artificial intelligence dalam dunia kreativitas desain. Journal of Informatics and Communication Technology (Jict), 5(1), 159-168. https://doi.org/10.52661/j_ict.v5i1.164
Stepanov, A. (2024). A brief overview of existing neural network solutions and services for photographers. Journal of Digital Art & Humanities, 5(1), 31-47. https://doi.org/10.33847/2712-8149.5.1_3
Syahputri, R. A., & Nugraha, J. (2024). Student behavior in using artificial intelligence for Canva instant presentation. Journal of Office Administration: Education and Practice, 4(2), 119–134. https://doi.org/10.26740/joaep.v4n2.p119-134
Twomey, J., Ching, D., Aylett, M. P., Quayle, M., Linehan, C., & Murphy, G. (2023). Do deepfake videos undermine our epistemic trust? a thematic analysis of tweets that discuss deepfakes in the Russian invasion of Ukraine. Plos One, 18(10), e0291668. https://doi.org/10.1371/journal.pone.0291668
Vaccari, C., & Chadwick, A. (2020). Deepfakes and disinformation: exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Society, 6(1), 205630512090340. https://doi.org/10.1177/2056305120903408
Wang, H., Ai, L., Xia, Y., Wang, G., Xiong, Z., & Song, X. (2023a). Software‐based screening for efficient sgRNAs in Lactococcus lactis. Journal of the Science of Food and Agriculture, 104(2), 1200-1206. https://doi.org/10.1002/jsfa.12946
Wang, Q. (2024). Creation is not like a box of chocolates: why is the first judgment recognizing copyrightability of ai-generated content wrong? Grur International, 73(8), 772-777. https://doi.org/10.1093/grurint/ikae082
Wang, T., & Chow, K. (2023). Noise based deepfake detection via multi-head relative-interaction. Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14548-14556. https://doi.org/10.1609/aaai.v37i12.26701
Wang, W., Xiao, H., & Fang, Y. (2024). Clothing image attribute editing based on generative adversarial network, with reference to an upper garment. International Journal of Clothing Science and Technology, 36(2), 268-286. https://doi.org/10.1108/ijcst-09-2023-0129
Wang, X., Guo, H., Hu, S., Chang, M.-C., & Lyu, S. (2023b). Gan-generated faces detection: a survey and new perspectives. Frontiers in Artificial Intelligence and Applications, 372. https://doi.org/10.3233/faia230558
Westerlund, M. (2019). The emergence of deepfake technology: a review. Technology Innovation Management Review, 9(11), 39-52. https://www.researchgate.net/publication/337644519_The_Emergence_of_Deepfake_Technology_A_Review
Zanardelli, M., Guerrini, F., Leonardi, R., & Adami, N. (2022). Image forgery detection: a survey of recent deep-learning approaches. Multimedia Tools and Applications, 82(12), 17521-17566. https://doi.org/10.1007/s11042-022-13797-w
Zhou, K. (2022). Ethical challenges and coping strategies of new media network modeling of photojournalistic images. International Journal of Science and Engineering Applications, 11(12), 376-378. https://doi.org/10.7753/ijsea1112.1041
Ziakis, C., & Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: insights from a comprehensive review. Information, 14(12), 664. https://doi.org/10.3390/info14120664