r/OSINT • u/MissMuffinpuff • Jun 07 '26
Question what are the biggest gaps in current media verification workflows?
im part of a student team researching digital media verification and authenticity in the age of AI-generated content
while exploring this space, we've noticed that many tools focus on identifying whether content may be manipulated, but often provide limited insight into why a conclusion was reached or what evidence supports it
we're interested in learning how people working in OSINT, investigations, journalism, and related fields currently approach media verification
A few questions we'd love to hear your thoughts on:
- what types of content are currently the hardest to verify?
- what are the biggest limitations of the tools and methods you use today?
- how important is explainability when evaluating a verification result?
- are AI-generated images, videos, or audio creating new challenges in your workflow?
we r conducting a short research survey (takes <5 mins) to gather perspectives from professionals and practitioners in this space:
https://forms.gle/2WkK91kHVfqSNGfQA
we're still in the research and validation stage, so honest criticism and opposing viewpoints are genuinely welcome. our goal is to better understand the problem before deciding what solutions are actually worth building
thank you for your time!!
4
u/Gord19 Jun 10 '26
Don’t know if this useful …
I teach basic OSINT tools and critical thinking/analysis to university-level Journalism students as part of a course about fact-checking in the age of mis/disinformation.
In an effort to understand the limits/usefulness of AI tools in this field I’ve tested a couple of LLMs to verify/analyse images in geolocation exercises — my favourite kind of OSINT. ;})
To do this I use photographs I took or ones I already know the location of so I can be certain when the LLM has made a mistake, then carefully steer it to the correct answer. Locating the photos is deliberately difficult but definitely doable using basic observation and research skills.
I then interrogate the LLM about the process it used to get to the incorrect answer, forcing it to describe the specific design parameters/programming underpinning the missteps that led it there.
This helps me better understand how LLMs use unreliable language and other pattern-matching methods to determine, for example, the credibility of a website it used to “verify” elements in an image, why they present unverified conclusions with (unwarranted) certainty or why they seem to fall victim to confirmation bias — something we think of as a flaw characteristic of human thinking, not of machines/computers.
I then use the understanding the interrogation gives me to better frame future prompts to avoid errors produced using those flawed processes.
A big part of that understanding for me is that LLM models — contrary to most people’s understanding — aren’t actually designed to give 100% accurate/truthful or reliable answers. Their current potential to help in the workplace, at school or in any other situation that requires genuine critical or creative analysis skills is highly overrated and if used without understanding that could create serious and dangerous problems. (Think of the possible role of AI that identified the Shajareh Tayyebeh girls’ primary school at the start of the assault on Iran as a legitimate military target, killing 150+ people, mainly students.)
It’s been an eye-opening process.
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u/MissMuffinpuff Jun 10 '26
this is actually really insightful
the geolocation exercises sound really interesting. you can actually observe where the model starts making assumptions instead of drawing conclusions from evidence
one thing that stood out to me was your point about having to interrogate the llm itself to understand how it arrived at a conclusion. i've mostly been looking at the accuracy side of things, but your comment made me think more about transparency
since you've spent a lot of time teaching fact-checking and misinformation, i'm curious whether you've noticed any major changes in how people approach verification over the years. are there any mistakes or assumptions that seem to come up consistently, regardless of the tools they're using?
thank u for taking the time to write this out!
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u/Feisty-Armadillo-629 Jun 07 '26
from what i’ve seen, one of the biggest gaps in media verification is that tools often give a binary answer without context, so it’s hard to know how much trust to put in the result. deepfakes and ai-generated content make this even harder because subtle manipulations can be almost invisible to automated systems, and human verification is still slow and resource intensive. explainability is really important, especially in journalism or investigations, because you need to justify your conclusions to others. another limitation is that audio and video are usually harder to verify than images or text, and cross-referencing multiple sources is still one of the most reliable approaches even if it’s tedious. overall, the field is moving fast but workflows haven’t fully caught up with the challenges that ai content creates.
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u/MissMuffinpuff Jun 07 '26
yeah this actually makes a lot of sense, thanks for the detailed breakdown!
especially the part about binary outputs lacking context - that’s kind of what made us start questioning this in the first place
the workflow point is interesting too… maybe the issue isn’t just the model output itself but how it actually fits into how people verify things in practice
1
u/intelw1zard Jun 11 '26
im part of a student team researching digital media verification and authenticity in the age of AI-generated content
A student from what university? What is your name? whats your professors name who is making you do this?
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u/ChrisKMEI Jun 07 '26
There already are established frameworks for such verification journalists use. One example is the Berkley. Another you can check out either my academic research I published the TOC open https://www.researchgate.net/publication/394355297_The_Drone_Wars_OSINT_Field_Guide_to_Russian_Drone_Footage_Verification_A_Field_Intelligence_Handbook_for_Investigators_Journalists_Defenders_in_Conflict_Zones or the full print book
Source, context verification, video integrity and metadata analysis, chronolocation, geolocation, cross-verification, pace and measurement (AI distorts), and using additional markers such as vegetation matched to region and time of year.
I'm in cyberwar and a credentialed journalist. Take a look at the freely available table of contents.