The Robot Dog Video That Looks Like Science Fiction Is Not AI-Generated

 

On June 3, 2026, a 32-second video was posted on X by a user named @myinvesthor. The video allegedly shows footage from a military exercise in China. It depicts four-legged, armed robotic dogs equipped with submachine guns mounted on their backs, reportedly being tested by a Chinese defense company. The accompanying post states that these robots are designed to enhance soldier safety in conflict zones and provide operational superiority through direct fire support.

 

 

At first glance, the video resembles a scene from a science fiction movie. Moreover, certain visual elements could suggest that it was generated using artificial intelligence. For this reason, its authenticity was carefully examined. The footage was analyzed using Google SynthID and HIVE Moderation, both AI-content detection tools. The results from both analyses indicated that the video contains authentic footage and was not generated using artificial intelligence. In other words, the video posted on X appears to be genuine.

The video also contains Chinese text and multiple watermarks. An examination of these elements suggests that the footage may have originated from a commercial or government-sponsored promotional event organized by a defense technology company specializing in unmanned tactical systems.

To further identify the manufacturer of the robotic systems shown in the video, individual frames were analyzed using Google’s reverse image search. The analysis revealed that the footage was first shared on April 1, 2026, by @Eng_china5 on X. It was also determined that the robots were produced by a private company operating in Shandong province, China.

In conclusion, the analysis indicates that the video contains authentic footage and was not generated using artificial intelligence. The armed robotic systems featured in the video highlight the increasing integration of autonomous and semi-autonomous technologies into military operations. In this respect, the footage provides insight into next-generation systems that may shape future conflict environments.

 

 

If you suspect that a video, image, or audio file has been created using artificial intelligence or deepfake technology and would like free assistance in verifying its authenticity, you may send the link to the content or the file itself to allaboutdeepfake@gmail.com.

Video Linked to Attacks in Ukraine Found to Be AI-Generated

On June 2, 2026, Russian drones and missiles reportedly targeted Kyiv, the capital of Ukraine, and several other cities during the early morning hours. According to reports, the attacks resulted in at least 18 fatalities and more than 100 injuries in Kyiv and other affected areas. Images, videos, and updates related to the attacks were widely circulated across numerous social media platforms. One such post was shared on June 2, 2026, by the user @EthanLevins2 on X.

 

The 46-second video was viewed more than 12,600 times and reposted 102 times within a short period. It also received 773 likes. However, subsequent investigations revealed that the footage had been generated using artificial intelligence. The video was analyzed using HIVE Moderation, which confirmed that the content had been created with AI-based tools.

In conclusion, the short video posted on X on June 2 was artificially generated and does not depict authentic events. Technical analysis identified several inconsistencies characteristic of AI-generated media. This case illustrates how visual content associated with breaking news and ongoing conflicts can spread rapidly when presented as genuine footage. Particularly during periods of war, conflict, and crisis, it is essential to verify the authenticity of shared videos and images through reliable sources and technical detection tools.

 

 

If you suspect that a video, image, or audio file has been created using artificial intelligence or deepfake technology and would like free assistance in verifying its authenticity, you may send the link to the content or the file itself to allaboutdeepfake@gmail.com.

The News Claiming That 119 Israeli Soldiers Were Arrested Was Generated Using Artificial Intelligence

An image posted on Facebook regarding Israel’s ground operation against Lebanon on April 9, 2026, appeared to be an image accompanying a news report.

 

 

The image was subsequently shared by numerous accounts on X and Instagram. On X in particular, a version posted by the user @Rizvana_Raza on April 10 was viewed 33,700 times, reposted 683 times, and received more than 2,000 likes.

According to the accompanying claim, 119 Israeli Army logistics personnel, along with trucks carrying ammunition and fuel, had surrendered to Hezbollah. The post described this as one of the largest logistical surrenders in recent history and asserted that Israel’s supply lines had collapsed.

However, the image is not authentic and was generated using artificial intelligence. The photograph was first analyzed using Google SynthID, which identified multiple indicators of AI generation. In particular, irregularities in the physical features of individuals depicted in the image—such as unnatural hand and facial structures—along with inconsistencies in text, logos, and background details, strongly suggested synthetic production through various AI tools.

The image was also analyzed using HIVE Moderation, which independently confirmed that it had been created using artificial intelligence.

 

 

A reverse image search further indicated that the alleged news story was not published by any recognized or reputable news organization. Consequently, it was determined that both the image and the accompanying claim, shared on Facebook on April 9 and later on X on April 10, were not authentic and had been generated using artificial intelligence.

If you suspect that a video, image, or audio file has been created using artificial intelligence or deepfake technology and would like free assistance in verifying its authenticity, you may send the link to the content or the file itself to allaboutdeepfake@gmail.com.

Video Allegedly Showing Ukrainian Drones Over Moscow Was Generated Using Artificial Intelligence

On June 1, 2026, a 37-second video was posted on YouTube by @CuongLuong-r2n. The video purported to show Ukrainian drones flying over Moscow, the capital of Russia. The accompanying description claimed that Ukraine had launched an attack on Moscow using more than 400 unmanned aerial vehicles (UAVs). The same video was subsequently uploaded on YouTube on June 2, 2026, by another user, @strker2000.

 

 

However, subsequent investigations revealed that both videos were generated using artificial intelligence. The footage was analyzed using HIVE Moderation, which determined that the content had been created with AI-based tools.

Further examination using Google SynthID indicated that the videos did not depict authentic footage but were instead generated through artificial intelligence or related digital technologies. The analysis highlighted several indicators of synthetic production, including unnatural drone flight patterns and multiple visual inconsistencies, which strongly suggested AI generation.

 

At the conclusion of both YouTube videos, it was noted that the content had previously been shared on Instagram, indicating that YouTube was not the original source. A reverse image search was therefore conducted, revealing that the video was first uploaded to Instagram on June 1, 2026, by “NEWS_02025.” The Instagram version received 505 likes and 55 comments within less than 24 hours.

 

In conclusion, the video initially shared on Instagram and subsequently reposted on YouTube was generated using artificial intelligence and does not depict real events. This case demonstrates how AI-generated content can reach large audiences through cross-platform reposting and gradually be perceived as authentic footage. Particularly in the context of international conflicts and security-related issues, it is essential to verify visual content using technical detection tools and cross-check it against reliable sources.

 

 

If you suspect that a video, image, or audio file has been created using artificial intelligence or deepfake technology and would like free assistance in verifying its authenticity, you may send the link to the content or the file itself to allaboutdeepfake@gmail.com.

AI-Generated Video Circulates on Reddit

Following the war that began with the U.S. and Israel’s attack on Iran on February 28, 2026, claims that Israel would target Türkiye after Iran have become a frequently discussed topic in Turkish public discourse. Numerous experts have shared their views on this issue across various platforms.

However, the debate has not been limited to experts; it has also become a widespread topic of public discussion. The heightened attention from both experts and the general public has led to an increase in social media posts suggesting that Türkiye would be the next target after Iran.

Not all of these posts reflect accurate information; some contain disinformation, fabricated claims, or misleading interpretations. One such post was shared on May 6, 2026, on Reddit by a user identified as “borsavefon.” The video purportedly showed Israeli journalist Yoni Ben-Menachem making statements about Türkiye.

 

Within three hours, the video received 45 comments and more than 44 upvotes. However, subsequent analysis determined that the footage had been produced using deepfake technology. The video was first examined using Google SynthID, which identified multiple indicators of AI generation. Notably, the speaker’s lip movements in the first half of the video appeared unnatural, and there were observable inconsistencies between the audio and visual synchronization—common characteristics of deepfake content.

The video was also analyzed using HIVE Moderation, another AI detection tool. The results corroborated the findings from Google SynthID, confirming that the content had been generated using AI algorithms.

 

In conclusion, the video posted on Reddit on May 6 was artificially generated and does not depict authentic statements. This case underscores the importance of verifying content circulating on social media platforms, particularly during periods of geopolitical tension and uncertainty. It also highlights the growing need for vigilance against AI-generated manipulative content.

 

 

If you suspect that a video, image, or audio file has been created using artificial intelligence or deepfake technology and would like free assistance in verifying its authenticity, you may send the link to the content or the file itself to allaboutdeepfake@gmail.com.

AI-Generated Explosion Video Circulates on Social Media

 On May 31, 2026, a 13-second video was posted on X by @Persianserene1. The post claimed that the footage depicted an explosion following an attack on Iran by U.S. and Israeli aircraft.

The video garnered significant attention, accumulating over 94,100 views, more than 4,700 likes, and over 1,300 reposts. However, subsequent investigation revealed that the footage was generated using artificial intelligence. An analysis conducted with HIVE Moderation confirmed that the content was AI-generated.

 

The post on X indicated that the video had previously been shared on Instagram, suggesting that it did not originate on X and had circulated across multiple social media platforms. A reverse image search was therefore conducted, which determined that the video was first uploaded to Instagram on May 30, 2026, by a user identified as “javadi__1364.”

In conclusion, the short video shared on X on May 31 was artificially generated and does not depict a real event. This case illustrates how AI-generated content can be recirculated by different users across various social media platforms for disinformation purposes. It underscores the critical importance of verifying the authenticity of visual content, particularly during periods of conflict and geopolitical tension.

If you suspect that a video, image, or audio file has been created using artificial intelligence or deepfake technology and would like free assistance in verifying its authenticity, you may send the link to the content or the file itself to allaboutdeepfake@gmail.com.

Academic Literature On Deepfakes And Related Topıcs In Taiwan: A Structural Topic Modeling Analysis Of Emerging Research Themes

 Abstract

Since its emergence in 2017, deepfake technology has evolved from a niche innovation into a global concern with significant implications for politics, security, ethics, and privacy. Its ability to generate synthetic yet hyper-realistic content—including video, audio, text, and images—has made it a powerful tool for both creative applications and malicious activities such as disinformation, fraud, and sexual exploitation. Taiwan, which has been repeatedly targeted by deepfake-driven disinformation campaigns and non-consensual content, presents a particularly critical case for understanding how academia engages with the challenges posed by this technology. This study conducts one of the first systematic analyses of academic literature on deepfakes in Taiwan, examining the characteristics, evolution, and thematic focus of the research. Using Structural Topic Modeling (STM) and web-scraping techniques, 143 academic studies—including journal articles, master’s theses, doctoral dissertations, book chapters, and institutional reports—were analyzed to identify dominant research trends and their development over time. The results indicate that academic interest in deepfakes in Taiwan has grown rapidly since 2019, with the majority of publications written in Chinese. Ten major thematic clusters were identified, primarily focusing on detection algorithms, machine learning applications, and legal frameworks for regulating deepfakes. However, the analysis also revealed a relative lack of interdisciplinary studies addressing psychological and sociopolitical aspects—areas more prevalent in global deepfake research. Comparatively, Taiwanese scholarship demonstrates a strong emphasis on technological and legal countermeasures rather than on societal impacts or public perception. Overall, the study highlights Taiwan’s increasing but technically focused academic engagement with deepfakes and emphasizes the need for expanded cross-disciplinary collaboration. Strengthening policy-oriented, ethical, and sociotechnical research will be essential for developing comprehensive national strategies to mitigate the multifaceted risks posed by deepfake technology.

LINK: https://dergipark.org.tr/tr/pub/dasad/article/1797836

AI-driven fraud as an emerging cyber risk: Evidence from a global incident-based analysis

The rapid proliferation of artificial intelligence (AI) and deepfake technologies has introduced new and complex risks to individuals, companies, financial systems, and digital trust. While existing research has primarily examined deepfakes in sexual or political contexts, systematic analyses of AI-enabled fraud remain limited. This study addresses this gap by conducting an incident-based analysis of 167 documented cases of AI- and deepfake-enabled fraud worldwide between 2019 and 2025. Drawing on Cyber-Routine Activities Theory (C-RAT), the study examines temporal trends, victim targeting patterns, financial losses, and cross-national variations to assess how emerging AI technologies reshape opportunity structures for cybercrime. The findings reveal a sharp increase in AI-assisted fraud after 2022, coinciding with the public availability of generative AI tools. Victimization patterns shifted from companies toward individuals, while financial losses initially concentrated among companies before increasingly affecting individual victims. Country-level analysis highlights substantial variation, including evidence that targeted regulatory interventions can reduce exposure to AI-enabled fraud, as demonstrated in the People’s Republic of China. Overall, the results support C-RAT’s core assumptions regarding motivated offenders, suitable targets, and capable guardianship, while extending the theory to account for AI-driven cyber threats and systemic forms of guardianship. The study emphasizes that AI-enabled fraud represents a structural social risk inherent in modern digital infrastructures. Effective mitigation requires multi-layered strategies integrating technical controls, organizational investment in cybersecurity, and adaptive regulatory governance.

LINK: https://www.tandfonline.com/doi/full/10.1080/07366981.2026.2631066

Deepfake Survey

I have prepared a survey as part of my postdoctoral research project at the Taiwan Foundation for Democracy. I would be very grateful for your participation. All participant information will remain strictly confidential and will not be disclosed under any circumstances. The results will not be used in any academic publication without prior ethical approval and will be used solely for the Taiwan Foundation for Democracy’s final report.

LINK: https://docs.google.com/forms/d/e/1FAIpQLSfrpkGSoYSb9lc68nKh5iHEJ8bkHabyKPQXvKvwegMhAtZRZQ/viewform?usp=dialog

Evaluating web-based deepfake detection tools: Risks, limits, and practical solutions for information security

ABSTRACT

The rapid advancement of artificial intelligence (AI) has facilitated the rise of deepfakes—synthetic audio, image, text, and video content generated by deep learning algorithms that can convincingly fabricate events, statements, or identities. While deepfake technology has positive applications in fields such as education and entertainment, it has predominantly been exploited for malicious purposes, including sexual exploitation, political manipulation, fraud, and disinformation campaigns. In response, web-based deepfake detection tools have emerged as accessible alternatives to algorithmic models, enabling non-experts such as researchers, journalists, and policymakers to verify suspicious content. However, systematic evaluations of these tools remain scarce in the academic literature. To address this gap, the present study examines 37 web-based deepfake detection tools. Employing descriptive and statistical analyses—including accuracy analysis, weighted score, and margin of error calculations—the study evaluates tool performance and categorizes them into high, moderate, and low accuracy groups. Results indicate that photo detection tools are the most reliable, with several achieving weighted scores above 80 percent, while video detection tools demonstrate only moderate effectiveness. Audio detection remains underdeveloped, showing significant weaknesses in both accuracy and tool availability. Hive Moderation stands out as the most versatile platform, though none of the tools offer comprehensive, multi-modal, or real-time detection. The findings highlight critical gaps in current detection capabilities, particularly in audio and in large, long-form media. This underscores the importance of scalable, multi-modal, real-time solutions to safeguard democratic institutions, organizations, companies, individual security, and public trust.

LINK: https://www.tandfonline.com/doi/full/10.1080/07366981.2025.2564783

Deepfake Interest in South Korea: A Temporal Analysis of Google Trends from 2017 to 2024

Deepfake technology, which utilizes artificial intelligence to generate hyper-realistically manipulated videos, images, texts, and audio, has garnered significant public and academic interest. The proliferation of deepfakes, especially in non-consensual pornography, financial fraud and political misinformation, has sparked ethical, moral, legal, and security debates worldwide. While existing research predominantly focuses on deepfake detection, legal frameworks, and their potential impact on the democratic process, few studies have examined public interest in deepfakes and the factors influencing search behavior. This study addresses this gap by analyzing public interest in deepfakes in South Korea, using Google Trends data from January 2017 to August 2024. This timeframe is particularly significant as it encompasses the initial emergence of deepfake technology in 2017 and its increasing use in fraudulent and non-consensual content in South Korea. The country represents a unique case due to its global leadership in deepfake-related searches, widespread consumption of non-consensual sexual deepfakes, and frequent occurrence of deepfake fraud. This study employs dictionary-based text analysis to categorize search queries into three main themes: sexual content, techniques for creating deepfakes, and methods for accessing deepfake materials. The findings indicate that 77.81% of searches are related to non-consensual sexual content, primarily targeting female celebrities. Contrary to global trends, political deepfakes did not significantly influence search patterns in South Korea. These insights highlight the urgent need for stronger regulatory frameworks and technological interventions to mitigate the harms associated with deepfakes.

LINK: https://dergipark.org.tr/tr/pub/ikad/issue/90816/1570974

Bridging Disciplinary Gaps and Methodological Challenges in Understanding Deepfake Discourse: A Study of Turkish Reddit Posts Related to Deepfake

Abstract

This study explores the evolving landscape of deepfake discussions, focusing particularly on Turkish posts on Reddit. While deepfake technology gained prominence in 2017, the academic community's interest surged, with studies primarily concentrating on deepfake detection. However, there is a dearth of research on public perceptions and online discourse surrounding deepfake. Through text analysis, this study explores Turkish Reddit posts, uncovering that a substantial portion (69.4%) focuses on deepfakes with sexual content, particularly featuring celebrities (60.2%). Politics also emerges as a prominent theme, constituting 22% of the content, with an additional 8.6% dedicated to technical aspects of deepfake creation. Over time, posts on sexual deepfake content, particularly those featuring celebrities, dominate, but during the May 2023 presidential and general elections, political deepfakes gained traction. Despite shedding light on content trends, the study faces two significant limitations. The first limitation is methodological. Studies relying solely on text analysis struggle to offer comprehensive insights into people's perceptions of deepfake. Therefore, a more profound understanding can be attained through qualitative face-to-face interviews, allowing for a deeper exploration of individuals' perspectives. The second limitation is disciplinary. Research encompassing diverse disciplines such as psychology, sociology, and media studies would yield more nuanced results in comprehending both the broader framework of public and societal perceptions on the topic and their deeper intricacies.

Key words: Deepfake, Türkiye, Reddit, Interdisciplinary Approach, Dictionary-based Text Analysis, Mixed Methods

Link: https://ojs.library.carleton.ca/index.php/J-ICSLAC/article/view/4839/3551

A Comprehensive Analysis of Public Discourse and Content Trends in Turkish Reddit Posts Related to Deepfake

ABSTRACT 

This study investigates the content and changes in deepfakes-related discussions on 5,220 Turkish Reddit posts from October 2019 to August 2023. Although the academic community has shown an increasing interest in deepfakes since 2017, focusing on detection methods and the technology itself, scant attention has been paid to public perceptions and online debate. The analysis reveals that 69.4% of the examined posts feature deepfake content with sexual themes, with celebrity women being the primary targets in 60.2% of cases. In contrast, 22% of the content is about politics and political figures, while 8.6% provides technical guidance on creating deepfakes. The study also observes content changes over time, noticing a rise in sexually explicit deepfake posts, particularly involving celebrities. However, in May 2023, coinciding with the presidential and general elections in Türkiye, discussions about politics and political figures have significantly increased. This study sheds light on the changing landscape of discussions, emphasizing the predominant presence of sexual content and the increasing prevalence of political content, particularly during election seasons. 

LINK: https://iga.pknu.ac.kr:446/html/jga/online-jga.php?mode=view&idx=28