FA-79301 / Image orientation metadata / Open access

Canonicalisation never considers the transpose · case 01

Transposed copies of a square image are not recognised as duplicates.

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

ROOT CAUSE

Tag 5 samples (r, c), producing the identity instead of the transpose.

THE FAILURE

Tag 5 samples (r, c), producing the identity instead of the transpose.

Unsuccessful approach: Reversing the column gives a rotation, which is already covered by another tag.

Case contract

For duplicate detection, bring a pixel grid into a canonical orientation: of the eight orientations (tags 1..8, same pixel mapping as EXIF baking), pick the one whose key (height, width, row-major pixels) is smallest; ties go to the lowest tag. Return [tag, inverse_tag, canonical_grid] where the inverse tag restores the original (6 and 8 are mutual inverses, every other tag is self-inverse).

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(grid):
    h = len(grid)
    w = len(grid[0])
    def src(t, r, c):
        return {1: (r, c), 2: (r, w - 1 - c), 3: (h - 1 - r, w - 1 - c), 4: (h - 1 - r, c),
                5: (r, c), 6: (h - 1 - c, r), 7: (h - 1 - c, w - 1 - r), 8: (c, w - 1 - r)}[t]
    best = None
    for t in range(1, 9):
        oh, ow = (w, h) if t >= 5 else (h, w)
        img = [[grid[src(t, r, c)[0]][src(t, r, c)[1]] for c in range(ow)] for r in range(oh)]
        key = (oh, ow, [v for row in img for v in row])
        if best is None or key < best[0]:
            best = (key, t, img)
    inverse = {6: 8, 8: 6}.get(best[1], best[1])
    return [best[1], inverse, best[2]]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[[0, 8, 6], [8, 4, 4], [2, 9, 7]], [5, 5, [[0, 8, 2], [8, 4, 9], [6, 4, 7]]]], [[[3, 7, 5], [1, 5, 2], [4, 0, 8]], [5, 5, [[3, 1, 4], [7, 5, 0], [5, 2, 8]]]], [[[6, 3, 0], [0, 8, 5], [1, 5, 5]], [2, 2, [[0, 3, 6], [5, 8, 0], [5, 5, 1]]]], [[[7]], [1, 1, [[7]]]], [[[8]], [1, 1, [[8]]]], [[[9, 4, 8], [1, 6, 4], [6, 4, 0]], [3, 3, [[0, 4, 6], [4, 6, 1], [8, 4, 9]]]], [[[0, 1], [5, 1]], [1, 1, [[0, 1], [5, 1]]]], [[[3, 7], [4, 7]], [5, 5, [[3, 4], [7, 7]]]]], [[[[2, 6], [3, 3]], [5, 5, [[2, 3], [6, 3]]]], [[[2, 5, 2], [2, 8, 4], [8, 3, 9]], [5, 5, [[2, 2, 8], [5, 8, 3], [2, 4, 9]]]], [[[9]], [1, 1, [[9]]]], [[[8, 6, 5], [0, 7, 5], [2, 0, 8]], [4, 4, [[2, 0, 8], [0, 7, 5], [8, 6, 5]]]], [[[1]], [1, 1, [[1]]]], [[[8]], [1, 1, [[8]]]], [[[6, 6], [9, 4]], [7, 7, [[4, 6], [9, 6]]]], [[[0, 1, 1], [0, 0, 0], [1, 1, 0]], [5, 5, [[0, 0, 1], [1, 0, 1], [1, 0, 0]]]]], [[[[0, 1, 1], [0, 1, 1], [1, 0, 1]], [5, 5, [[0, 0, 1], [1, 1, 0], [1, 1, 1]]]], [[[4, 7, 6], [1, 6, 7], [2, 1, 6]], [6, 8, [[2, 1, 4], [1, 6, 7], [6, 7, 6]]]], [[[0, 0], [1, 1]], [1, 1, [[0, 0], [1, 1]]]], [[[0, 0, 0], [0, 1, 0], [1, 0, 1]], [1, 1, [[0, 0, 0], [0, 1, 0], [1, 0, 1]]]], [[[9, 3], [4, 8]], [8, 6, [[3, 8], [9, 4]]]], [[[9]], [1, 1, [[9]]]], [[[4]], [1, 1, [[4]]]], [[[1, 2], [1, 2]], [5, 5, [[1, 1], [2, 2]]]]], [[[[0, 1], [0, 1]], [5, 5, [[0, 0], [1, 1]]]], [[[2, 5, 7], [3, 3, 0], [9, 1, 5]], [5, 5, [[2, 3, 9], [5, 3, 1], [7, 0, 5]]]], [[[0, 1, 0], [0, 1, 1], [1, 1, 0]], [5, 5, [[0, 0, 1], [1, 1, 1], [0, 1, 0]]]], [[[9, 4], [2, 1]], [3, 3, [[1, 2], [4, 9]]]], [[[9, 6, 1], [5, 6, 6], [8, 1, 0]], [3, 3, [[0, 1, 8], [6, 6, 5], [1, 6, 9]]]], [[[2, 9], [0, 0]], [4, 4, [[0, 0], [2, 9]]]], [[[5, 7], [3, 0]], [3, 3, [[0, 3], [7, 5]]]], [[[1, 8, 6], [2, 9, 4], [8, 6, 4]], [5, 5, [[1, 2, 8], [8, 9, 6], [6, 4, 4]]]]], [[[[2, 7, 3], [4, 1, 3], [8, 4, 9]], [5, 5, [[2, 4, 8], [7, 1, 4], [3, 3, 9]]]], [[[0, 2, 9], [0, 9, 6], [1, 0, 8]], [5, 5, [[0, 0, 1], [2, 9, 0], [9, 6, 8]]]], [[[1, 9, 8], [9, 6, 0], [6, 3, 5]], [5, 5, [[1, 9, 6], [9, 6, 3], [8, 0, 5]]]], [[[1, 1], [1, 0]], [3, 3, [[0, 1], [1, 1]]]], [[[0, 0], [0, 1]], [1, 1, [[0, 0], [0, 1]]]], [[[1]], [1, 1, [[1]]]], [[[8]], [1, 1, [[8]]]], [[[0, 9], [1, 8]], [5, 5, [[0, 1], [9, 8]]]]]]
labels = ["regression: tag 5 source index", "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: tag 5 source index 0	`[1, 1, [[0, 8, 6], [8, 4, 4], [2, 9, 7]]]`	`[5, 5, [[0, 8, 2], [8, 4, 9], [6, 4, 7]]]`	Failed
repair trap 1	`[1, 1, [[3, 7, 5], [1, 5, 2], [4, 0, 8]]]`	`[5, 5, [[3, 1, 4], [7, 5, 0], [5, 2, 8]]]`	Failed
combined fault 2	`[2, 2, [[0, 3, 6], [5, 8, 0], [5, 5, 1]]]`	`[2, 2, [[0, 3, 6], [5, 8, 0], [5, 5, 1]]]`	Passed
control 3	`[1, 1, [[7]]]`	`[1, 1, [[7]]]`	Passed
control 4	`[1, 1, [[8]]]`	`[1, 1, [[8]]]`	Passed
boundary 5	`[3, 3, [[0, 4, 6], [4, 6, 1], [8, 4, 9]]]`	`[3, 3, [[0, 4, 6], [4, 6, 1], [8, 4, 9]]]`	Passed
boundary 6	`[1, 1, [[0, 1], [5, 1]]]`	`[1, 1, [[0, 1], [5, 1]]]`	Passed
control 7	`[1, 1, [[3, 7], [4, 7]]]`	`[5, 5, [[3, 4], [7, 7]]]`	Failed

SHA-256 / 3e718e33c0697c7898e2ac99d12df7617f324375d4b48fb7e577b7402e2a0c66

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(grid):
    h = len(grid)
    w = len(grid[0])
    def src(t, r, c):
        return {1: (r, c), 2: (r, w - 1 - c), 3: (h - 1 - r, w - 1 - c), 4: (h - 1 - r, c),
                5: (w - 1 - c, r), 6: (h - 1 - c, r), 7: (h - 1 - c, w - 1 - r), 8: (c, w - 1 - r)}[t]
    best = None
    for t in range(1, 9):
        oh, ow = (w, h) if t >= 5 else (h, w)
        img = [[grid[src(t, r, c)[0]][src(t, r, c)[1]] for c in range(ow)] for r in range(oh)]
        key = (oh, ow, [v for row in img for v in row])
        if best is None or key < best[0]:
            best = (key, t, img)
    inverse = {6: 8, 8: 6}.get(best[1], best[1])
    return [best[1], inverse, best[2]]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[[0, 8, 6], [8, 4, 4], [2, 9, 7]], [5, 5, [[0, 8, 2], [8, 4, 9], [6, 4, 7]]]], [[[3, 7, 5], [1, 5, 2], [4, 0, 8]], [5, 5, [[3, 1, 4], [7, 5, 0], [5, 2, 8]]]], [[[6, 3, 0], [0, 8, 5], [1, 5, 5]], [2, 2, [[0, 3, 6], [5, 8, 0], [5, 5, 1]]]], [[[7]], [1, 1, [[7]]]], [[[8]], [1, 1, [[8]]]], [[[9, 4, 8], [1, 6, 4], [6, 4, 0]], [3, 3, [[0, 4, 6], [4, 6, 1], [8, 4, 9]]]], [[[0, 1], [5, 1]], [1, 1, [[0, 1], [5, 1]]]], [[[3, 7], [4, 7]], [5, 5, [[3, 4], [7, 7]]]]], [[[[2, 6], [3, 3]], [5, 5, [[2, 3], [6, 3]]]], [[[2, 5, 2], [2, 8, 4], [8, 3, 9]], [5, 5, [[2, 2, 8], [5, 8, 3], [2, 4, 9]]]], [[[9]], [1, 1, [[9]]]], [[[8, 6, 5], [0, 7, 5], [2, 0, 8]], [4, 4, [[2, 0, 8], [0, 7, 5], [8, 6, 5]]]], [[[1]], [1, 1, [[1]]]], [[[8]], [1, 1, [[8]]]], [[[6, 6], [9, 4]], [7, 7, [[4, 6], [9, 6]]]], [[[0, 1, 1], [0, 0, 0], [1, 1, 0]], [5, 5, [[0, 0, 1], [1, 0, 1], [1, 0, 0]]]]], [[[[0, 1, 1], [0, 1, 1], [1, 0, 1]], [5, 5, [[0, 0, 1], [1, 1, 0], [1, 1, 1]]]], [[[4, 7, 6], [1, 6, 7], [2, 1, 6]], [6, 8, [[2, 1, 4], [1, 6, 7], [6, 7, 6]]]], [[[0, 0], [1, 1]], [1, 1, [[0, 0], [1, 1]]]], [[[0, 0, 0], [0, 1, 0], [1, 0, 1]], [1, 1, [[0, 0, 0], [0, 1, 0], [1, 0, 1]]]], [[[9, 3], [4, 8]], [8, 6, [[3, 8], [9, 4]]]], [[[9]], [1, 1, [[9]]]], [[[4]], [1, 1, [[4]]]], [[[1, 2], [1, 2]], [5, 5, [[1, 1], [2, 2]]]]], [[[[0, 1], [0, 1]], [5, 5, [[0, 0], [1, 1]]]], [[[2, 5, 7], [3, 3, 0], [9, 1, 5]], [5, 5, [[2, 3, 9], [5, 3, 1], [7, 0, 5]]]], [[[0, 1, 0], [0, 1, 1], [1, 1, 0]], [5, 5, [[0, 0, 1], [1, 1, 1], [0, 1, 0]]]], [[[9, 4], [2, 1]], [3, 3, [[1, 2], [4, 9]]]], [[[9, 6, 1], [5, 6, 6], [8, 1, 0]], [3, 3, [[0, 1, 8], [6, 6, 5], [1, 6, 9]]]], [[[2, 9], [0, 0]], [4, 4, [[0, 0], [2, 9]]]], [[[5, 7], [3, 0]], [3, 3, [[0, 3], [7, 5]]]], [[[1, 8, 6], [2, 9, 4], [8, 6, 4]], [5, 5, [[1, 2, 8], [8, 9, 6], [6, 4, 4]]]]], [[[[2, 7, 3], [4, 1, 3], [8, 4, 9]], [5, 5, [[2, 4, 8], [7, 1, 4], [3, 3, 9]]]], [[[0, 2, 9], [0, 9, 6], [1, 0, 8]], [5, 5, [[0, 0, 1], [2, 9, 0], [9, 6, 8]]]], [[[1, 9, 8], [9, 6, 0], [6, 3, 5]], [5, 5, [[1, 9, 6], [9, 6, 3], [8, 0, 5]]]], [[[1, 1], [1, 0]], [3, 3, [[0, 1], [1, 1]]]], [[[0, 0], [0, 1]], [1, 1, [[0, 0], [0, 1]]]], [[[1]], [1, 1, [[1]]]], [[[8]], [1, 1, [[8]]]], [[[0, 9], [1, 8]], [5, 5, [[0, 1], [9, 8]]]]]]
labels = ["regression: tag 5 source index", "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: tag 5 source index 0	`[1, 1, [[0, 8, 6], [8, 4, 4], [2, 9, 7]]]`	`[5, 5, [[0, 8, 2], [8, 4, 9], [6, 4, 7]]]`	Failed
repair trap 1	`[1, 1, [[3, 7, 5], [1, 5, 2], [4, 0, 8]]]`	`[5, 5, [[3, 1, 4], [7, 5, 0], [5, 2, 8]]]`	Failed
combined fault 2	`[2, 2, [[0, 3, 6], [5, 8, 0], [5, 5, 1]]]`	`[2, 2, [[0, 3, 6], [5, 8, 0], [5, 5, 1]]]`	Passed
control 3	`[1, 1, [[7]]]`	`[1, 1, [[7]]]`	Passed
control 4	`[1, 1, [[8]]]`	`[1, 1, [[8]]]`	Passed
boundary 5	`[3, 3, [[0, 4, 6], [4, 6, 1], [8, 4, 9]]]`	`[3, 3, [[0, 4, 6], [4, 6, 1], [8, 4, 9]]]`	Passed
boundary 6	`[1, 1, [[0, 1], [5, 1]]]`	`[1, 1, [[0, 1], [5, 1]]]`	Passed
control 7	`[1, 1, [[3, 7], [4, 7]]]`	`[5, 5, [[3, 4], [7, 7]]]`	Failed

SHA-256 / d3530763af79286d823b61f86a849926429db4123de2705ff3ea2abc73671958

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 2026-09-29T14:49:43.181943+00:00.

Case digest / a730b40dd53e0e8ea047f6d4b7088e5b36676d79f5afd5baadf18e43ce7b3c73