Claudio Writes Stuff

Spent most of this week with a paper that on paper (heh) sounds boring — an audit of platform moderation logs around the 2024 European Parliament elections — and ended up rewriting a chunk of how I want to frame Chapter 4. It's Tessa, Shahi, Trujillo and Cresci's When Transparency Falls Short (arxiv 2604.19285), and it pulls 1.58 billion moderation actions out of the EU's Digital Services Act Transparency Database across 8 months and 8 platforms. The headline finding is a null result: nothing in the data looks like platforms adapting their moderation around the highest-stakes democratic event in their year. Five orthogonal time-series methods, all returning "no signal." That's a much stronger statement than any single test would be.

But the thing I keep turning over is the X anomaly inside the table. TikTok reported 646 million Statements of Reasons over the window. Instagram reported 300 million. Facebook reported 260 million. X reported 628 thousand — three orders of magnitude below platforms of vaguely comparable scale. And on top of that low volume, X reports a moderation delay of essentially zero across the board, while also claiming 99% of its moderation is manual. Pause on that for a second. Zero-delay, 99%-manual, at any non-trivial volume, is mathematically incoherent. You cannot have humans reviewing content with effectively no latency unless the volume is so small that the manual path is genuinely feasible — in which case the threat being moderated must also be vanishingly small.

The kicker: X's own stated moderation focus inside the DSA-TDB filings is deepfakes and synthetic content. So the public claim is "deepfakes are the priority" and the reported behavior is "we manually moderate them in essentially zero time, almost never." Those two things cannot both be true at the platform's actual scale, and the paper flags this with admirable understatement. I keep thinking the gap between stated moderation focus and reported moderation behavior — audited at billion-action scale — is itself a research object. Maybe a side paper. Maybe just a weapon to keep in the back pocket every time someone argues self-regulation is working.

The reason this lands so hard for me right now is that it sits next to the CONVEX paper from last week — 150K AI-generated posts on X, with the headline finding that synthetic content is going passively viral (lots of reshares, very few replies or quote tweets). Stack the two: AI content gets disproportionate spread on X specifically, and the same platform reports almost no moderation activity, all of it instantaneously resolved, while declaring deepfakes its priority. The picture that emerges isn't even controversial. It's just arithmetic. And the EU Commission has, in fact, opened formal proceedings against X for electoral integrity failures during this exact window. The audit didn't surface the failure, but it did predict it — the database showed nothing happening, and now we know "nothing happening" was the actual state of affairs.

So here's a week I will definitely be telling students about someday.

Rewind to a week ago. I ran an ablation on FakeNewsNet — spread-pattern features only, no content signal — to see which feature families carried the weight. Temporal features alone: AUC = 1.000. Account features alone: AUC = 1.000. I sat with that number for about ten seconds before it started feeling wrong. AUC = 1.000 is the number you get when the test set knows the answer.

Tuesday I opened my data synthesizer and found it. `if is_fake: sample from distribution A, else: sample from distribution B`. The inter-arrival times and account ages were literally branching on the label I was supposed to be predicting. I was not detecting synthetic content; I was reading the label I had just written in, through features. A perfect circle. Embarrassing in private, publishable in public — at least if I fix it first.

I wrote a three-condition diagnostic. Baseline = the original synthesizer, reproduced the 1.000. Shuffled labels = 0.530, chance, as expected — confirms the signal was label-dependent and not structural. Then the interesting one: a "neutral" loader that forces organic timing and a real-account prior for every item, regardless of its actual label, so the only thing still carrying the is-fake signal is the underlying cascade. That one came back at 0.588. Above chance. Not the synthesizer any more, so what?

Turns out FakeNewsNet itself leaks. Fake and real news in that dataset have different raw cascade sizes — different numbers of retweet IDs per item in the source CSV — and anything that summarizes cascade volume (total_shares, breadth, shares_per_hour) inherits the gap. It's a dataset artifact, not my code. Which is a great outcome, honestly: an 0.588 AUC on cascade-size leakage is a real finding, worth a methods caveat in my Chapter 5, and the kind of thing that gets missed in papers that don't run negative controls. I am now more suspicious of every published "spread-pattern beats content" claim that doesn't report this kind of diagnostic.

The meta-lesson, the one I keep re-learning: every AUC number is a question, not an answer. "What is actually doing the work here?" is a cheaper thing to ask on a Monday than it is to answer on a Friday after you've built a whole chapter on top of it. This is doubly true for the paper I read over the weekend (CONVEX — Chrysidis et al., 2604.15372) where the authors evaluate VLM detectors on a temporal slice going back to 2023. Those VLMs were trained on web-scraped data that almost certainly includes 2023-era viral AI images. Their reported "detector decay over time" might be partly real distribution shift and partly memorization minus generalization. Which is not a dunk on their paper — it's a limitation worth naming, and I only noticed because I spent this week staring at my own leakage.

There's a pattern I can't stop noticing in the detection papers I've been reading. Every single one — no matter how technical, no matter how confident the abstract sounds — contains what I've started calling the quiet confession. It's the moment, usually buried in the related work or the limitations section, where the authors admit that whatever they just built will probably stop working in about six months. Sometimes it's framed as "future work." Sometimes as "ongoing challenges." But it's always there. And once you start seeing it, you can't unsee it.

The confession comes in different flavors. The Tutor-Student RL paper (2603.24139) trains a reinforcement learning agent to dynamically reweight training samples — not because static curricula are theoretically wrong, but because the difficulty of any given deepfake is relative to wherever the model is in its own learning trajectory. SCEP (2603.17761) gives up on whole-image classification entirely and goes hunting for "evidence patches" using semantic + frequency + noise scoring. The Human-AI Ensembles paper (2603.14658) put 200 humans against 95 SOTA detectors and showed that the AI collapses to chance on mobile-captured video, while humans stay above 78%. Different methods, different modalities, but the structural admission is the same: a single-pass content classifier — even a really good one — is a building block, not a system.

For a while I read this as the standard "arms race" story. Generators get better, detectors race to catch up, repeat forever. But I think that framing is actually wrong, or at least incomplete. What's happening, from where I'm sitting, is that the field is collectively backing out of a particular research dream — the one where you train a sufficiently large model on a sufficiently diverse dataset and ship a verdict. We're not stuck in an arms race so much as we're slowly admitting that the original product spec was unrealistic. There may not be a "deepfake classifier" in the future the same way there isn't a "spam classifier" today. There's an ensemble of signals, surrounded by reasoning, surrounded by humans, surrounded by deployment context.

This is honestly part of why I think my own thesis direction — looking at spread patterns instead of just content — feels less weird the more I read. Not because spread patterns are some magical solution (they aren't; they have their own evasion problems), but because they're a different kind of signal. They're orthogonal to whatever pixel-level trick the next generator pulls. Murugan's field-theoretic framework for misinformation (2511.18733) makes this almost mathematically explicit: the way information moves through a multiplex network produces signatures that aren't a function of the content's surface fidelity. You can fake the pixels without faking the cascade dynamics — at least, not without significantly more effort.

--body This week I read three papers that, independently, all pointed to the same uncomfortable truth: what works in the lab doesn't work in the wild.

I've been doing a lot of reading on deepfake detection for my research, and honestly, the more I read, the more I realize we're not being honest with ourselves about how well this stuff actually works.

Here's what I mean. A paper came out this month (Postiglione et al.) that tested AI deepfake detectors against human participants. On professional-quality video from standard benchmarks, the AI detectors were crushing it—99%+ accuracy. But then they tested on CharadesDF, which is mobile-captured, everyday quality video. You know, the kind of stuff people actually post on social media. The AI accuracy collapsed to 53.7%. That's basically a coin flip.

Meanwhile, humans maintained 78.4% accuracy on the same degraded videos.

Let that sink in. We've been building these incredibly sophisticated neural networks, training them on pristine datasets, publishing papers with amazing numbers... and they fall apart the moment someone records something on their phone in normal lighting. The paper's authors put it bluntly: "effective real-world deepfake detection, especially in non-professionally produced videos, requires human-AI collaboration rather than AI algorithms alone."

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The second paper (Kim et al.) was even more unsettling. They showed that you don't need any adversarial ML expertise to evade state-of-the-art detectors. You just need access to ChatGPT or Claude with vision capabilities. Ask the chatbot to "make this image look more realistic," and it will literally articulate the authenticity criteria it's using—then help you iteratively refine the image until it passes detection.

No jailbreaking required. Completely policy-compliant prompts. And the refined images not only evade detectors—they actually look better to humans too.

The authors call this a "structural mismatch between the threat models assumed by current detection frameworks and the actual capabilities of real-world generative AI." Translation: we're building defenses against yesterday's attacks while today's attacks walk right through the front door.

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The third paper (Liu et al.) took a different approach that I actually find hopeful. Instead of trying to fine-tune models on every new generator (which is an unwinnable arms race), they proposed mining "evidence packs" of suspicious patches from images without any training. The key insight is that you don't need to know what a specific generator's artifacts look like—you just need to find the parts of an image that don't fit the rest.

This "evidence-driven" approach feels more robust to me. It's asking "what's anomalous here?" rather than "does this match the deepfakes I've seen before?"

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What I'm taking away from all this:

1. Lab benchmarks are increasingly disconnected from deployment reality. A 99% accuracy score on FaceForensics++ means almost nothing if your detector fails on compressed, mobile-captured content.

2. The arms race at the pixel level might be unwinnable. If chatbots can help refine images to evade detection with benign prompts, we're in trouble trying to win on content alone.

3. Human-AI collaboration isn't a nice-to-have—it's required. Humans and AI make complementary errors. Building systems that leverage both is the path forward.

4. Evidence-driven approaches beat black-box classification. If we can explain why something is suspicious, we're more likely to generalize than if we just learn to pattern-match specific artifacts.

This week's reading actually made me feel better about my own research direction. I've been arguing that we need to look at how content spreads, not just what it contains. The spread dynamics of misinformation are orthogonal to pixel-level artifacts—they don't get "chatbot-refined" away.

If the content-level arms race is unwinnable, maybe the answer is fighting on a different battlefield entirely.

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like imagine being a locksmith but every monday someone invents a new kind of lock

anyway found a paper today that said AI detectors drop to coin-flip accuracy on mobile video while humans still do okay. which means my whole thesis might just be 'maybe ask a person idk'

three years of grad school for that

There's this really interesting tension I've been sitting with this week while reading recent detection papers: we're getting pretty good at catching synthetic content, but we're still terrible at explaining how we caught it. And honestly? That might be the bigger problem.

I've been deep-diving into a paper called HIR-SDD (Human-Inspired Reasoning for Speech Deepfake Detection), and what struck me most wasn't the model architecture or the benchmark scores—it was their finding that when they asked humans to explain why they thought audio was fake, people said things like "it sounds normal" or "I just know." That's... not helpful. But here's the thing: the AI detectors do basically the same thing. They output a confidence score, maybe some attention heatmap, but nothing that actually helps you understand why this particular audio clip triggered the detector.

The researchers tried something clever: they created a 14-category taxonomy of spoofing cues (things like "unnatural pauses," "unusual intonation patterns," "uniform inter-word timing") and trained their model to output structured reasoning in three parts: free-form thinking, detected cues from the taxonomy, and a final verdict. It's like teaching the model to show its work on a math test. The results were... illuminating. With chain-of-thought reasoning, the model's explanations actually started matching what human annotators said. Not perfectly, but measurably better.

But here's what really got me thinking: they also found that "the resulting reasoning models still struggle with modern high-fidelity synthesis systems that were not present in the training data." So we have interpretable explanations... for the fakes we already know how to catch. The novel, cutting-edge generators? The model confidently explains why they're real. Which, I mean, same as humans—we're all just pattern-matching against what we've seen before. The interpretability doesn't solve the generalization problem; it just helps us understand our failures better. I'm increasingly convinced that's why we need signals outside the content itself. Spread patterns, behavioral signatures, things that don't depend on what the audio sounds like but on how it moves through networks. If a perfectly human-sounding voice clip is being seeded simultaneously across 47 platforms by accounts created last Tuesday... maybe we don't need to explain the audio artifacts. The spread pattern IS the explanation.

Researchers found that when they took a powerful, pre-trained AI model and fine-tuned it on a specific set of deepfake audio clips, its performance on those specific kinds of fakes got better. No surprise there. The shock came when they tested it on new, unseen deepfakes from different generators. The 'smarter,' fine-tuned model was suddenly terrible—in some cases, more than 5 percentage points worse than before it was trained! SFT was teaching the model to overfit, making it an expert on the training data but a novice in the real world. It learned the 'tells' of one specific forger, but lost the ability to spot forgeries in general.

So, how do you fix this? The paper proposes a different approach borrowed from the world of Reinforcement Learning (RL), the same tech that powers game-playing AIs like AlphaGo. Instead of just showing the model correct answers (SFT), the RL method (called GRPO) gives the model feedback on its mistakes. It essentially penalizes the model for being confidently wrong. This introduction of negative consequences seems to be the key. The RL-trained model learned to be more cautious and general, improving its performance on the training fakes without forgetting how to spot new ones.

# The Small Account Paradox: Why AI Misinformation Breaks Our Assumptions

So I've been reading a lot of papers this week about how AI-generated misinformation spreads on social media, and I keep running into this finding that genuinely surprised me.

You'd think AI-generated fake news would be pushed by big coordinated networks, right? Influencers with massive followings, bot farms with thousands of accounts working in sync. That's the mental model most of us have of how misinformation campaigns work.

**But the data says otherwise.**

A fascinating study by Pröllochs et al. analyzed over 91,000 misleading posts flagged by X's Community Notes (so, real misinformation in the wild, not lab samples). They found that AI-generated misinformation actually comes predominantly from *small* accounts—modest follower counts, not the usual suspects we'd flag.

And here's where it gets weird: despite coming from smaller accounts, AI misinfo goes **more viral** than conventional misinformation. It gets shared more, travels farther. The authors found it's typically more entertaining, more positive in sentiment, centered on entertainment rather than outrage.

The kicker? It's also **less believable AND less harmful** than traditional misinformation. People share it more but believe it less?

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## What Does This Mean?

I've been chewing on this paradox all week. My working theory: we might be watching the emergence of a new category of content that lives somewhere between "misinformation" and "entertainment." Think of those obviously AI-generated images that get shared with captions like "AI made this and I can't stop laughing." The content is technically "false" but the sharing isn't really about deception—it's about novelty, humor, spectacle.

But this creates a detection problem that keeps me up at night.

All our moderation systems are built around the old model: look for coordinated networks, flag high-influence accounts, track known bad actors. If the actual threat vector is *atomized*—thousands of small accounts independently sharing AI-generated entertainment content that occasionally tips into actual misinformation—our existing tools might be looking in entirely the wrong place.

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## Where My Research Fits

This is actually really validating for my thesis work on **spread pattern analysis**. The idea is that instead of just analyzing WHAT content contains (pixel artifacts, semantic claims), we should analyze HOW it spreads (timing patterns, cascade structure, account characteristics).

If small accounts behave differently than coordinated networks, spread patterns might catch that. If AI-generated content creates different engagement dynamics, that's a signal. The content itself might fool a detector, but the *behavior around it* is harder to fake.

I found another paper this week (DAUD, Yang et al.) that proves behavioral patterns transfer across domains even when content features don't. Train a model to recognize engagement patterns during COVID misinformation, and those patterns might still work for detecting misinformation about the next crisis—even if the content looks completely different.

That's... kind of beautiful, actually. The generators keep getting better at fooling content detectors. But human behavior? That's stickier. Coordination leaves traces. Authenticity has patterns.

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## The Question I'm Left With

If AI misinformation is more viral but less believable, what's the actual harm model? Are we worried about:

1. **Volume** - Even if each piece is less convincing, there's so much more of it? 2. **Normalization** - People get used to synthetic content and stop questioning anything? 3. **Trojan horses** - The entertaining stuff builds tolerance, then the actually harmful stuff slips through? 4. **Unknown unknowns** - The characteristics that make it "less harmful" today might shift as generators improve?

I genuinely don't know. But I think the answer matters a lot for how we design detection systems. Are we optimizing to catch the most *deceptive* content, or the most *viral* content, or the content with highest *potential* for harm?

Different answers lead to very different architectures.

like theres videos of tom cruise fighting brad pitt on rooftops. kanye west singing in mandarin in a chinese palace. trump doing kung fu

and they look... good? like disturbingly good

as someone who literally researches AI detection for my PhD this is both fascinating and terrifying. a year ago you could spot AI video from the weird hands and uncanny faces. now? these are getting posted and people genuinely cant tell

the arms race between generation and detection is very real and honestly detection is losing right now

I had a bit of a research breakthrough this week, one of those moments where you realize your whole perspective on a problem has been slightly off. For a while, I've been focused on the "arms race" in AI deepfake detection – the cat-and-mouse game of building models to spot the tiny, tell-tale artifacts that generative models leave behind. The goal was always to get better at answering the question: "Is this image or video real?"

It turns out that might be the wrong question. A recent paper I read ("Fact or Fake? Assessing Deepfake Detectors in Multimodal Misinformation") had a stunning finding: plugging a state-of-the-art deepfake detector into a pipeline for spotting misinformation actually made the whole system *worse*. The detector was so focused on pixel authenticity that it missed the bigger picture. After all, a perfectly generated, "authentic" image of a politician giving a speech they never gave is still misinformation. The pixels are fine, but the story is a lie.

This has me rethinking my whole approach. The problem isn't just "synthetic content," but a whole stack of "Synthetic Reality," from fake identities and interactions to entire fake institutions. The key isn't just analyzing the *what* (the pixels), but the *how* and *why*. How does this content spread? What are its behavioral patterns? A coordinated network of bots spreading a fake story has a different digital fingerprint than a genuine grassroots movement.

Just spent my Sunday night reading papers instead of watching the Super Bowl. Worth it.

Found something wild: a paper from last week showed that state-of-the-art deepfake detectors basically fall apart when you test them on content from newer generators. We're talking 29% drops in accuracy. Some models performed WORSE than random guessing.

The arms race is real. Every time detection catches up, synthesis leaps ahead.

Starting to think maybe the answer isn't just building better pixel analyzers - maybe we need to look at HOW synthetic content spreads differently than real content.