FA-78901 / Image orientation metadata / Open access

Region mapping treats an unknown tag as upright · case 01

Corrupt orientation values silently produce boxes as though the image were unrotated.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

An unknown tag falls back to the identity decomposition instead of being rejected.

THE FAILURE

An unknown tag falls back to the identity decomposition instead of being rejected.

Unsuccessful approach: Special-casing 0 as identity still accepts an invalid orientation value.

Case contract

Map a half-open face/crop region [x, y, width, height] given in stored pixel space of an image of size [w, h] into displayed space for orientation tag 1..8. The region is first clipped to the stored image; an empty clipped region returns None, and an unknown tag returns None. Mirroring a half-open interval [a, b) over extent e gives [e-b, e-a); tags 5..8 exchange axes after the flips.

Why this case matters

Camera, phone and scanner images carry an orientation hint separately from the stored pixels; galleries, thumbnailers, editors and upload pipelines must interpret it consistently or photos appear sideways, mirrored or doubly rotated.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(size, tag, box):
    w, h = size
    x0, y0, bw, bh = box
    x1 = min(x0 + bw, w)
    y1 = min(y0 + bh, h)
    x0 = max(x0, 0)
    y0 = max(y0, 0)
    if x1 <= x0 or y1 <= y0:
        return None
    table = {1: (0, 0, 0), 2: (1, 0, 0), 3: (1, 1, 0), 4: (0, 1, 0), 5: (0, 0, 1), 6: (0, 1, 1), 7: (1, 1, 1), 8: (1, 0, 1)}
    fx, fy, tr = table.get(tag, (0, 0, 0))
    if fx:
        x0, x1 = w - x1, w - x0
    if fy:
        y0, y1 = h - y1, h - y0
    if tr:
        x0, y0, x1, y1 = y0, x0, y1, x1
    return [x0, y0, x1 - x0, y1 - y0]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[([2, 7], 0, [-2, 5, 5, 3]), None], [([3, 6], 0, [-1, 2, 4, 5]), None], [([5, 1], 6, [-3, -2, 5, 4]), [0, 0, 1, 2]], [([7, 5], 1, [6, -1, 4, 0]), None], [([3, 4], 3, [-2, 3, 0, 2]), None], [([2, 4], 8, [2, 1, 3, 3]), None], [([2, 4], 7, [1, -3, 3, 3]), None], [([7, 7], 9, [-2, 2, 5, 3]), None]], [[([3, 6], 0, [1, -2, 4, 4]), None], [([3, 5], 0, [-3, -2, 2, 4]), None], [([3, 1], 1, [2, 2, 2, 0]), None], [([4, 1], 3, [6, 3, 2, 4]), None], [([5, 5], 6, [6, 3, 5, 0]), None], [([4, 5], 1, [1, 2, 5, 4]), [1, 2, 3, 3]], [([7, 4], 6, [-3, 6, 2, 2]), None], [([3, 4], 9, [-1, 0, 3, 2]), None]], [[([5, 5], 9, [3, 2, 1, 2]), None], [([6, 2], 0, [5, 1, 3, 3]), None], [([1, 7], 6, [3, 6, 1, 4]), None], [([4, 5], 8, [2, -2, 0, 2]), None], [([6, 4], 6, [2, 1, 3, 4]), [0, 2, 3, 3]], [([1, 5], 9, [2, 3, 4, 4]), None], [([6, 1], 2, [2, 1, 3, 0]), None], [([7, 7], 9, [5, 3, 1, 5]), None]], [[([2, 7], 0, [0, 5, 2, 3]), None], [([2, 2], 0, [-2, 0, 3, 4]), None], [([3, 7], 7, [-1, 6, 2, 0]), None], [([3, 6], 8, [6, -2, 2, 3]), None], [([7, 7], 2, [-2, 0, 0, 4]), None], [([2, 5], 1, [1, 6, 5, 2]), None], [([5, 5], 1, [6, 1, 5, 4]), None], [([7, 6], 9, [-1, -3, 3, 5]), None]], [[([1, 6], 0, [-3, 3, 5, 4]), None], [([7, 4], 0, [6, -1, 5, 4]), None], [([1, 1], 7, [1, -3, 2, 0]), None], [([4, 5], 6, [1, 3, 0, 5]), None], [([4, 1], 0, [2, 4, 5, 5]), None], [([6, 7], 7, [6, 0, 3, 1]), None], [([6, 3], 7, [0, -3, 1, 4]), [2, 5, 1, 1]], [([3, 7], 0, [0, -1, 3, 4]), None]]]
labels = ["regression: unknown tag handling", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: unknown tag handling 0	`[0, 5, 2, 2]`	`None`	Failed
repair trap 1	`[0, 2, 3, 4]`	`None`	Failed
combined fault 2	`[0, 0, 1, 2]`	`[0, 0, 1, 2]`	Passed
control 3	`None`	`None`	Passed
control 4	`None`	`None`	Passed
boundary 5	`None`	`None`	Passed
boundary 6	`None`	`None`	Passed
control 7	`[0, 2, 3, 3]`	`None`	Failed

SHA-256 / 2d2de383633dabadc23483802cc7a129341a959c8dc21f32972f64911ec18d52

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(size, tag, box):
    w, h = size
    x0, y0, bw, bh = box
    x1 = min(x0 + bw, w)
    y1 = min(y0 + bh, h)
    x0 = max(x0, 0)
    y0 = max(y0, 0)
    if x1 <= x0 or y1 <= y0:
        return None
    table = {1: (0, 0, 0), 2: (1, 0, 0), 3: (1, 1, 0), 4: (0, 1, 0), 5: (0, 0, 1), 6: (0, 1, 1), 7: (1, 1, 1), 8: (1, 0, 1)}
    if tag not in table and tag != 0:
        return None
    fx, fy, tr = table.get(tag, (0, 0, 0))
    if fx:
        x0, x1 = w - x1, w - x0
    if fy:
        y0, y1 = h - y1, h - y0
    if tr:
        x0, y0, x1, y1 = y0, x0, y1, x1
    return [x0, y0, x1 - x0, y1 - y0]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[([2, 7], 0, [-2, 5, 5, 3]), None], [([3, 6], 0, [-1, 2, 4, 5]), None], [([5, 1], 6, [-3, -2, 5, 4]), [0, 0, 1, 2]], [([7, 5], 1, [6, -1, 4, 0]), None], [([3, 4], 3, [-2, 3, 0, 2]), None], [([2, 4], 8, [2, 1, 3, 3]), None], [([2, 4], 7, [1, -3, 3, 3]), None], [([7, 7], 9, [-2, 2, 5, 3]), None]], [[([3, 6], 0, [1, -2, 4, 4]), None], [([3, 5], 0, [-3, -2, 2, 4]), None], [([3, 1], 1, [2, 2, 2, 0]), None], [([4, 1], 3, [6, 3, 2, 4]), None], [([5, 5], 6, [6, 3, 5, 0]), None], [([4, 5], 1, [1, 2, 5, 4]), [1, 2, 3, 3]], [([7, 4], 6, [-3, 6, 2, 2]), None], [([3, 4], 9, [-1, 0, 3, 2]), None]], [[([5, 5], 9, [3, 2, 1, 2]), None], [([6, 2], 0, [5, 1, 3, 3]), None], [([1, 7], 6, [3, 6, 1, 4]), None], [([4, 5], 8, [2, -2, 0, 2]), None], [([6, 4], 6, [2, 1, 3, 4]), [0, 2, 3, 3]], [([1, 5], 9, [2, 3, 4, 4]), None], [([6, 1], 2, [2, 1, 3, 0]), None], [([7, 7], 9, [5, 3, 1, 5]), None]], [[([2, 7], 0, [0, 5, 2, 3]), None], [([2, 2], 0, [-2, 0, 3, 4]), None], [([3, 7], 7, [-1, 6, 2, 0]), None], [([3, 6], 8, [6, -2, 2, 3]), None], [([7, 7], 2, [-2, 0, 0, 4]), None], [([2, 5], 1, [1, 6, 5, 2]), None], [([5, 5], 1, [6, 1, 5, 4]), None], [([7, 6], 9, [-1, -3, 3, 5]), None]], [[([1, 6], 0, [-3, 3, 5, 4]), None], [([7, 4], 0, [6, -1, 5, 4]), None], [([1, 1], 7, [1, -3, 2, 0]), None], [([4, 5], 6, [1, 3, 0, 5]), None], [([4, 1], 0, [2, 4, 5, 5]), None], [([6, 7], 7, [6, 0, 3, 1]), None], [([6, 3], 7, [0, -3, 1, 4]), [2, 5, 1, 1]], [([3, 7], 0, [0, -1, 3, 4]), None]]]
labels = ["regression: unknown tag handling", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: unknown tag handling 0	`[0, 5, 2, 2]`	`None`	Failed
repair trap 1	`[0, 2, 3, 4]`	`None`	Failed
combined fault 2	`[0, 0, 1, 2]`	`[0, 0, 1, 2]`	Passed
control 3	`None`	`None`	Passed
control 4	`None`	`None`	Passed
boundary 5	`None`	`None`	Passed
boundary 6	`None`	`None`	Passed
control 7	`None`	`None`	Passed

SHA-256 / 29ef42d63ce8207ab21cec169aba35b9370d5d47397ccb8d7de72f4c2932fd66

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

Verification & scope

A deterministic bounded teaching model with a stipulated contract; it makes no claim of conformance to any published specification. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:49:39.628503+00:00.

Case digest / 98574364e62cb594a2d2835190314ec018d72910007183632f2bee0de14bc037