is-this-photo-real

Is this photo real

Verification order is the whole skill: provenance first, pixels last. Finding the
earliest copy and reading its caption settles more cases than every forensic filter
combined, and it produces evidence you can show someone. Pixel forensics produces a
colourful heatmap and an argument.

The beginner mistake is running error level analysis on a downloaded JPEG and announcing
the image is fake. The second is looking for deepfakes: the overwhelming majority of
deceptive media is real footage with a false caption — right pixels, wrong war,
wrong year, wrong country.

Triage: what question are you actually answering

The claim under test	Do this first	Not this
"This shows event X in place Y"	find-the-original-image, then geolocate-from-pixels	Any forensic filter. Recontextualisation leaves no pixel trace at all.
"This is an unaltered photograph"	Signal-level analysis, on the least-processed copy you can obtain	Analysing a screenshot or a platform download; both destroy the signal.
"This person said this on video"	Provenance, then audio-visual consistency, then face-boundary behaviour frame by frame	An AI-detector score.
"This image was AI-generated"	Absent camera physics and incoherent object structure	A detector verdict on its own.
"This screenshot is genuine"	Layout, font and interface-version consistency; the underlying record if one exists	Image forensics. Fabricated screenshots are made in a browser, not an image editor.

Then always ask what the image would look like if the claim were true, write it
down, and check for those things specifically. Verification tests a hypothesis;
hunting for anomalies fails, because anomalies are everywhere.

Method

1. Get the best copy. Every re-encode, resize and screenshot destroys forensic
   signal. Chase the original upload or the agency version, not the platform rendition.
   Hash it, work on copies. If all you have is a screenshot, say so and lower every
   downstream conclusion.
2. Provenance. Run find-the-original-image; for video, extract and search
   keyframes. You want an earlier appearance, a different caption, a photographer
   credit, and an on-page date corroborated through read-deleted-pages. An earlier copy
   with a different caption ends the case.
3. Metadata. Run secrets-in-file-metadata: editing chain, thumbnail-versus-image
   comparison, timestamp inconsistencies, whether MakerNotes fit the claimed device.
4. Provenance signing. Check for C2PA Content Credentials.
5. Internal consistency. Signage language, plates, currency, uniforms, vehicle
   models, season, weather and shadows against the claimed date and place, via
   geolocate-from-pixels. Ordinary detective work, more productive than forensics.
6. Physical consistency. Lighting, shadows, reflections, perspective.
7. Signal-level forensics. Noise residuals, JPEG quantisation and double compression,
   clone detection, colour-filter-array traces. Easy to over-read, worthless on a
   platform-processed file.
8. Write what you verified, not a verdict.

Tools and their failure modes: reference/tool-catalogue.md.
Ordered by cost: reference/verification-checklist.md.

Lighting, shadow and geometry — the checks that hold up

No tooling, reasoning you can explain to an editor or a court, immune to recompression.
This is where to spend your time. Shadow convergence is the strongest. Sunlight is
parallel, so in a perspective image, lines drawn from each shadow's tip through the base
of the object that cast it must all meet at one point — the projection of the light
source. Draw three or four. An object whose line refuses to meet the others was probably
not in the original scene; sloped ground is the confound, so use one plane only. Then:
shadow direction and penumbra hardness should be consistent across an outdoor scene;
specular highlights in eyes, glass and polished metal should agree on where the lights
are; a reflection must show what is in front of it, correctly placed and reversed;
parallel lines should converge on a common vanishing point with eye level consistent for
people on one ground plane; and real lenses leave an optical signature — consistent depth
of field, chromatic aberration at high-contrast edges, vignetting, a noise floor that
varies with brightness. An element carrying none of that, in an image that has it
elsewhere, was added.

Error level analysis, and why it is mostly used wrongly

ELA re-saves the image at a known JPEG quality and displays the difference, on the theory
that a region compressed a different number of times responds differently. Four reasons
it produces confident nonsense: it responds to content, so edges and texture light up
while flat sky and skin go dark, meaning every image has "suspicious bright regions"; one
re-save destroys it, so ELA on a social-media download describes the platform's encoder
and nothing earlier; it cannot localise a modern edit, because content-aware fill,
generative editing and a full re-save leave no differential history to find; and it fails
in both directions, with bright regions on untouched images and clean output on
manipulated ones both routine.

Where it earns its place: on a single-generation JPEG straight from a camera, a pasted
region from a differently-compressed source can genuinely show up. Narrow case. Use it
as one weak input, only on least-processed files, never as the basis of a published
claim. More defensible relatives — quantisation-table comparison against camera
signatures, and double-compression detection — are also defeated by platform processing.

C2PA and Content Credentials

C2PA binds a cryptographically signed manifest to a file recording capture and edit
history. Where it exists it is the strongest provenance evidence available, because it
is verifiable rather than inferential. A valid manifest means the signer asserts
this history, the file is unchanged since signing, and you know who to hold
responsible — not that the content is true. A signed photograph of a staged scene is a
signed photograph.

Absence means almost nothing: most cameras do not sign, most editing pipelines do
not preserve manifests, and platforms strip them during re-encoding. Missing
credentials are the default state, not a red flag. Same for the IPTC
digital-source-type field used to label synthetic media, and for model-specific
invisible watermarks — a positive is strong where you can check it, a negative only
says one vendor's mark was not found.

AI generation — durable tells and tells that rot

Anything resting on a model's current weaknesses will be fixed. Prefer tells
grounded in physics and structure.

Durable, because they need a world model the generator does not have: impossible
lighting (inconsistent shadow directions, missing shadows under objects, a subject
lit from a direction with no source); structural incoherence in background objects (a
bicycle frame that does not connect, a railing whose baluster spacing changes, stair
treads that do not line up, a strap that vanishes and resumes, patterned fabric whose
pattern ignores the folds); text degradation, especially small, repeated or peripheral
text; contact and occlusion errors, such as a hand around a cup that does not enclose
it, or feet not meeting the ground; absent camera physics — no sensor noise, no
chromatic aberration, uniform focus, and too little high-frequency detail, which is
what "over-smooth skin" actually is; and no plausible provenance at all.

Ages badly — check, but do not rest on: finger and tooth counts, ear asymmetry,
garbled foreground text, mangled jewellery, suspiciously symmetrical faces.

Detector tools. A confident score with no auditable reasoning. They false-positive
on compressed, resized, upscaled, heavily edited and low-light real photographs,
false-negative against generators newer than their training data, and are adversarially
fragile — mild recompression moves scores. Run more than one, treat them as a weak
signal, never publish a conclusion resting on one. If your evidence is a percentage from
a website, you have no evidence.

Video

• Container and encoder. ffprobe -show_format -show_streams and MediaInfo give the
  encoder string, frame rate, rotation matrix and track structure; values typical of a
  platform re-encode mean you do not have an original. ffprobe -show_frames exposes
  frame types — duplicated frames mean frame-rate conversion or inserted slow motion, an
  unexplained keyframe mid-way through a static shot can mark a splice, and interlacing
  or telecine artifacts reveal a pipeline nobody mentioned.
• Generation loss. Blockiness, banding and mosquito noise stack with each re-encode.
  Heavily degraded footage is old, widely copied, or both — and every signal-level test
  on it is void.
• Audio. Lip-sync drift, room acoustics that do not match the visible space,
  ambience that does not change when the camera goes indoors, noise-floor jumps at edit
  points. The weakest link in most fabricated video and the least examined.
• Face-swap behaviour. Flicker or blur at hairline and jaw, face lighting not
  tracking head movement, teeth and tongue degrading during speech, the face at a
  different resolution from the frame, breakdown on profile turns and hand occlusion.
• Keyframes to reverse search. The most common outcome of a video verification is
  finding the video, older, elsewhere. Extraction commands live in
  find-the-original-image.

Where this goes wrong

• Every filter has a base-rate problem. Run six forensic tools on an authentic
  photograph and something will look anomalous. Anomaly is the normal condition of
  real images.
• The platform did it. Resizing, re-encoding, chroma subsampling and metadata
  stripping produce artifacts people attribute to manipulation. Establish processing
  history before interpreting any artifact.
• You will be handed the worst copy — a screenshot of a repost of a crop. Most
  signal-level analysis is invalid on it, and the honest report says so.
• "Not manipulated" is not a finding. Absence of detected manipulation is a
  statement about your tests, not about the image.
• A real photo can be entirely misleading. Selective framing, staged scenes and
  a true image with a false caption all pass every forensic test.
• Debunking amplifies. A detailed refutation spreads the original claim — an
  editorial judgement worth making deliberately. Material is also sometimes seeded to be
  discovered and debunked, or to see who investigates; see
  investigate-without-getting-made.
• Identification from resemblance is the highest-consequence error here — "this is
  person Z because they look alike" is not a finding.

Confidence grading

Grade each claim separately — one image can have a confirmed origin, a contradicted
caption and unconfirmed authenticity at once.

• Confirmed original and correctly described — earliest copy located from a plausible
  originator, date independently corroborated, location visually verified, internal
  details consistent, nothing contradicted in metadata or geometry.
• Confirmed recontextualised — the same image demonstrably published earlier with
  a different, better-sourced caption. The most common positive finding here.
• Probably manipulated — a specific, describable physical or geometric
  inconsistency you can point at, ideally with a source for the inserted element.
  Not a heatmap.
• Probably synthetic — multiple durable generative tells, no provenance history,
  no camera-consistent compression or metadata, no earlier copies. Name the tells.
• Unconfirmed — no earlier copy found, nothing wrong found. Where most cases end.
• Cannot be assessed — the available copy is too processed for the tests the question
  needs. Say which tests were invalid and why.

Worked example

A video circulates showing an explosion, captioned as a strike on a named city that
morning.

yt-dlp pulls the best rendition rather than a screen recording. ffprobe shows a
frame rate and encoder string typical of a platform re-encode, so signal-level tests
are off the table — noted, not lamented.

Keyframes extracted at scene changes. Frame four hits nothing. Frame nine, the only one
with a skyline, hits a news site from eighteen months earlier in a different country.
Provisional answer in ten minutes. But the earlier page's video is shorter, so which is
the parent? The suspect version has a hard cut and a noise-floor jump in the audio four
seconds in, and the ambience after the cut does not match the visible space. Added
audio, not added video.

Dead end worth recording: an AI detector scored the frames as likely synthetic. They
are a re-encoded crop of real broadcast footage — exactly the input that fools these
tools. Discarded.

Findings: confirmed recontextualised, with read-deleted-pages corroborating the
earlier page's date; audio probably manipulated; authenticity of the underlying
video cannot be assessed at signal level given the copy available.

Reporting standard. State what you verified, what you could not, and what each
conclusion rests on — never a bare "fake" or "real". A defensible line reads: the
image was published at least three years before the claimed event, on a news site,
credited to a named photographer; the location matches that credit and not the claim; no
manipulation was detected, but the only available copy was a platform re-encode, so
signal-level tests were not meaningful. "Our analysis shows this is fake" is not.
Include the tests you ran and their negative results, the file's processing history, and
your assumptions, then hand the package to write-the-intel-brief.

Pivots

What you got	Send to
Earlier copies, credits, original caption	find-the-original-image
Editing chain, device, timestamps	secrets-in-file-metadata
Location and date verification	geolocate-from-pixels, where-was-this-taken
Deleted or altered source pages	read-deleted-pages
Publishing or seeding domain	who-owns-this-domain, recon-a-domain-passively
Accounts amplifying the media	hunt-a-handle, pattern-of-life-from-socials
Coordinated network behind the spread	graph-the-network, find-leaks-in-the-wild
Named individuals in or credited on the media	find-anyone

Legal and ethical notes

Publishing an accusation of fabrication against a named person carries defamation
risk in most jurisdictions, and "our tool said so" is not a defence. Uploading
material to online forensic services discloses it to those services and, for some, to
public galleries — check whether a tool publishes submissions before submitting
anything sensitive. If the media depicts a crime, a victim, or intimate content:
minimise copies, do not redistribute, and in the case of child sexual abuse material
stop immediately and report to the appropriate authority rather than analysing it. See
../../ETHICS.md.