The core insight behind the research is not new. For nearly a decade, researchers have demonstrated that encrypted traffic leaks information through its side channels: packet sizes, timing patterns, and the bursty structure of adaptive video streaming all leave characteristic traces that survive encryption. Landmark work such as the 2017 study Beauty and the Burst showed that remote observers could identify encrypted video streams, and subsequent studies extended the technique to platforms like YouTube, including attacks that could name the specific title a user was watching. What has changed is the scale and the operational reality of these systems. Most published identification algorithms rely on static machine learning models, which means that whenever the pattern database is modified, for example when a new film or series is added to the catalogue, the entire model must be retrained from scratch, a process that consumes large amounts of time and computational power.

That limitation matters because streaming catalogues are anything but static. New content appears daily, encoding parameters shift, and traffic patterns evolve with every codec update and delivery-network change. A classifier trained last month may already be out of date. Fesl and his colleagues, working under the long-term VideoStream Hunter project financed by the Czech national research and education network operator CESNET, set out to build an identification system that could grow and adapt as easily as the catalogues it monitors. Their answer is a family of deep-learning models based on Siamese neural networks, an architecture originally developed for one-shot image recognition and since applied to tasks ranging from wireless signal classification to recommendation systems.

The Siamese design is what gives the new approach its scalability. Instead of learning to assign each traffic sample to one of a fixed set of classes, a Siamese network learns a similarity function: it takes two inputs, embeds each into a common representation space, and decides how similar they are. In the context of encrypted video identification, one input is a fingerprint extracted from live network traffic and the other is a reference fingerprint from the pattern database. If the network judges them similar enough, the stream is identified as the corresponding title. The crucial consequence is that adding a new video to the database does not require retraining the network at all. The model has learned what similarity looks like in general, so new fingerprints can simply be enrolled, much like adding a new face to a face-recognition gallery without teaching the system to see again.

A second pillar of the work is data. As part of their long-term research programme, the team created a new dataset containing several thousand fingerprints of real video streams captured from network traffic probes. This is a significant contribution in a field where many studies rely on laboratory recordings or public datasets that may not reflect the messiness of production networks, where adaptive bitrate switching, background traffic, and VPN tunnelling all distort the signals. The dataset builds on the group’s earlier published encrypted network video stream dataset, and it allowed the researchers to test their models under realistic conditions rather than idealised ones. The paper’s authors acknowledge the University of South Bohemia’s Faculty of Science and the Czech Technical University’s Faculty of Information Technology for providing the technical equipment for the experiments, with Tomáš Macák responsible for the experimental part.

The technical challenge the fingerprints must overcome is substantial. Modern streaming platforms deliver video using adaptive protocols such as DASH and increasingly over the QUIC transport protocol, which fragments and re-encodes content dynamically in response to network conditions. Two viewers watching the same film may produce noticeably different packet sequences depending on their bandwidth, device, and player implementation. Earlier approaches have attacked this problem with a variety of tools, including Markov probability fingerprints, differential fingerprints, low-dimensional embeddings, Levenshtein-distance clustering, and ensemble classifiers, each achieving respectable accuracy under specific assumptions. The Siamese approach differs philosophically: rather than modelling each video’s traffic pattern as a fixed signature, it learns the deeper structure that makes two traces of the same content recognisably related even when their surface features differ.

In their evaluation, the researchers report that the accuracy of their Siamese models is close to the current best-known solutions in the field, while avoiding the lengthy training process those solutions require. That trade-off is the heart of the paper’s contribution. A system that matches state-of-the-art accuracy but can absorb new fingerprints on the fly is far more practical for real-world deployment, whether by network operators monitoring traffic for capacity planning, by security teams hunting for policy violations, or by researchers studying how streaming platforms behave at scale. The dynamic, highly scalable character of the method means the pattern database can be modified without the expensive retraining cycles that have made static classifiers brittle in production environments.

The implications cut in two directions, and both deserve attention. On one side, the work is a pointed reminder that encryption alone does not guarantee viewing privacy. An observer positioned anywhere along the path between a viewer and a streaming server, at an ISP, a corporate gateway, or a public Wi-Fi access point, can in principle determine what that viewer is watching without ever breaking the encryption itself. This is a classic side-channel attack, and the growing sophistication of the techniques, documented across a series of surveys and studies of encrypted traffic analysis, means that the gap between what encryption promises and what it delivers for metadata privacy continues to widen. Countermeasures such as traffic padding, obfuscation, and constant-rate transmission exist, but they carry real costs in bandwidth and efficiency, which is why platforms have been slow to adopt them.

On the other side, the same technology has legitimate and valuable uses. Network operators need to understand traffic composition to provision capacity and diagnose quality-of-service problems, and encrypted traffic increasingly blindsides those tools. Content-delivery networks and regulators may need to verify that licensed content is being delivered as contracted. The Siamese architecture’s ability to scale gracefully makes such applications more feasible, because the identification system can track a catalogue of thousands of titles without collapsing under the weight of its own retraining schedule. The research also connects to a broader trend in machine learning, where metric-learning approaches that compare examples directly are displacing fixed-class classifiers in domains where the set of categories is large, open-ended, and constantly changing.

For the field of encrypted traffic analysis, the study signals a shift in what matters most. The question is no longer only whether encrypted video can be identified, which has been answered affirmatively many times over, but whether identification systems can keep pace with the real world’s churn. By pairing a large, realistic dataset of several thousand real-stream fingerprints with an architecture that treats identification as similarity comparison rather than classification, the Czech team has moved the state of the art closer to operational reality. As streaming continues to dominate global internet traffic and encryption becomes ever more universal, the contest between the metadata we inevitably leak and the tools that can exploit it is only going to intensify, and this work shows that the tools are getting faster, more flexible, and harder to outgrow.

Subject of Research: Identification of encrypted network video streams using Siamese neural networks

Article Title: Dynamic highly-scalable approach for encrypted network videostream identification

Article References: Dynamic highly-scalable approach for encrypted network videostream identification. (n.d.). https://doi.org/10.1007/s10586-026-06584-x

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06584-x

Keywords: encrypted traffic analysis, video stream identification, Siamese neural networks, deep learning, network privacy, side-channel attacks, SSL/TLS, streaming platforms, traffic fingerprints, machine learning, network security, dataset

The timing of the research could hardly have been more charged. In October 2025, Dave Meltzer of the Wrestling Observer Newsletter reported that AI had entered WWE’s creative process, and months later TKO Group Holdings President Mark Shapiro acknowledged that Chief Content Officer Paul Levesque, better known as Triple H, uses AI in storylines. WWE clarified that its current AI use is largely confined to video edits, highlight packages and audio noise reduction, but for fans who had spent years reading between the lines of every backstage leak, the distinction felt almost irrelevant. The genie, as the researchers put it, was already out of the bottle.

Professional wrestling occupies a unique position in this debate because it is arguably the most liminal genre in popular culture, neither pure sport nor pure theatre but a hybrid resting on a fragile contract known as kayfabe. Kayfabe is the sustained illusion that rivalries and victories are real, even when everyone knows they are scripted. It is not about deception but about collective buy-in. The study asks what happens when the presumed writer is no longer human but an algorithm trained on decades of television transcripts, and whether AI in effect turns heel by breaking wrestling’s oldest covenant.

Using netnography, a form of ethnographic research adapted for online communities, the authors analysed public discourse across three subreddits, r/SquaredCircle, r/WWE and the satirical r/SCJerk, over a ten-week period between November 2025 and May 2026. They gathered live event threads, conducted keyword searches for terms such as AI and ChatGPT, and applied snowball sampling until every identified topic reached saturation. Reflexive thematic analysis following Braun and Clarke’s six-phase procedure produced three dominant themes, each revealing a different strategy by which fans negotiate algorithmic authorship.

The first theme the researchers call the forensic gaze, a heightened form of textual and visual inspection in which fans evaluate content not by whether it advances a storyline but by whether a human created it at all. The phenomenon crystallised around the April 2026 NXT video package featuring Zaria, which fans overwhelmingly condemned for its AI-generated appearance, citing asynchronous mouth movement, missing background blur and a plastic sheen on skin textures. More than 150 separate comment threads debated the package, with side-by-side comparisons to older, human-made WWE videos serving both as evidence and as demonstrations of community authority. The gaze extended to language itself, with fans claiming they could identify machine-written promos by their symmetric sentences, absence of colloquialisms and lack of heat behind the words.

Yet the forensic gaze also uncovered an uncomfortable truth. A minority of fans confessed that formulaic human writing had become so predictable that they could not reliably distinguish it from machine output. As one commenter asked, would AI really be worse than the fifteenth identical cookie-cutter promo of the year? This liminal case, the authors argue, suggests that algorithmic kayfabe can taint the viewing experience retrospectively, raising the possibility that humans have already been performing something close to algorithmic work long before machines arrived to formalise it.

The second theme centres on affective rejection, condensed in the label fans gave to unwanted AI content: AI slop. The term carries theoretical weight beyond its insult value, connoting waste, matter out of place and something generated without intent, care or craft rather than created. Outrage spiked after the NXT video packages, and 62 mentions of AI slop appeared on r/SquaredCircle compared with 34 on r/WWE and only 12, mostly sarcastic, on r/SCJerk. The researchers connect this fury to the moral economy of creative labour, noting comments such as the observation that WWE will pay Roman Reigns twenty million dollars but will not pay a video editor sixty thousand. Fans, the study finds, have become political economists of culture, framing AI authorship not merely as poor output but as dispossession.

Beneath the anger lies what the authors identify as an ontological violation. Fans distinguished carefully between the first fake, the deliberate theatrical artifice of kayfabe in which they knowingly participate, and a second fake, an algorithmic simulation of that artistry with no human intention behind it. One fan summarised it bluntly: the problem is not that wrestling is fake, which is obvious, but that when AI writes it, there is no human performance behind it at all. Drawing on Goffman’s frame analysis, the researchers describe this as a keying of a keying, a simulation of a simulation that audiences cannot trace and therefore cannot re-enter, rupturing the ritual contract between performer and spectator.

The third and most theoretically generative theme is parodic absorption, exemplified by r/SCJerk, where fans neither detect nor rage but instead swallow the AI threat whole through elaborate comedy. Users crafted a fictional AI-written return storyline for Bobby Lashley obsessed with Japanese culture, an absurd promo that mocked the real AI’s ignorance of wrestling continuity while exceeding its failures with deliberate ridiculousness. By producing funnier AI promos than the machines themselves, fans affirmed their own creative agency, and the community’s memes, such as the phrase AI is the new authority, allowed members to engage with the technology through strategic ambiguity, never committing to acceptance or rejection. The researchers call this an anti-affordance, a community-enforced discursive constraint that channels suspicion into irony, and they describe humour here as grassroots ontological repair, a communal mechanism for managing anxiety about a future no individual fan can control.

The study’s culminating contribution is the concept of dialectical algorithmic kayfabe. Rather than a single rupture in wrestling’s ritual contract, the authors argue that algorithmic kayfabe unfolds as an ongoing oscillation between detection, outrage and ironic repair, moments that coexist across platforms and can even be experienced by the same fan in the same evening. The framework extends affordance theory by showing that platform norms shape not just how fans speak but which emotions are legally acceptable in each space, and it advances Foucault’s author function by revealing its forensic side, in which fans deny authorship to any text lacking traces of human labour. As the researchers conclude, machines may increasingly help produce wrestling’s stories, but they hold no jurisdiction over togetherness, the collective performance of fandom that remains stubbornly, defiantly human.

Subject of Research: How WWE fans on Reddit perceive and negotiate AI-generated or AI-assisted wrestling storytelling on streaming platforms

Article Title: A netnography of WWE fans grappling with AI storytelling on streaming platforms

Article References: Pandey, S., & Roy, A. (2026). A netnography of WWE fans grappling with AI storytelling on streaming platforms. Discover Global Society, 4(1), Article 249. https://doi.org/10.1007/s44282-026-00597-y

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00597-y

Keywords: WWE, artificial intelligence, netnography, fandom, streaming platforms, kayfabe, authenticity, Reddit, AI slop, algorithmic kayfabe, creative labour, digital culture