FAILURE MAP
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FA-79296 / Image orientation metadata / Open access

Canonical key compares pixels without the shape · case 01

A 2x3 image and its 3x2 rotation compare by raw pixel order, so different aspect ratios canonicalise inconsistently.

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

ROOT CAUSE

The comparison key drops the height and width.

VERIFIED REPAIR

Compare (height, width, pixels) so the shape decides first.

Unsuccessful approach: Ordering by width before height picks the landscape orientation where the contract picks the portrait one.

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: (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 = [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, 0], [1, 1], [0, 1]], [5, 5, [[0, 1, 0], [0, 1, 1]]]], [[[0], [0], [0]], [5, 5, [[0, 0, 0]]]], [[[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]]]], [[[6], [0], [9]], [5, 5, [[6, 0, 9]]]]], [[[[4], [3], [0]], [6, 8, [[0, 3, 4]]]], [[[4, 4]], [1, 1, [[4, 4]]]], [[[9], [0], [1]], [6, 8, [[1, 0, 9]]]], [[[1, 0], [1, 0]], [7, 7, [[0, 0], [1, 1]]]], [[[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]]]], [[[1, 1], [4, 4], [9, 1]], [8, 6, [[1, 4, 1], [1, 4, 9]]]]], [[[[5], [4]], [6, 8, [[4, 5]]]], [[[1, 1, 0], [1, 1, 0]], [2, 2, [[0, 1, 1], [0, 1, 1]]]], [[[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]]]], [[[6], [8]], [5, 5, [[6, 8]]]]], [[[[4], [5], [0]], [6, 8, [[0, 5, 4]]]], [[[3, 8, 5]], [1, 1, [[3, 8, 5]]]], [[[4], [4]], [5, 5, [[4, 4]]]], [[[6, 7, 5], [1, 9, 6], [6, 4, 6]], [8, 6, [[5, 6, 6], [7, 9, 4], [6, 1, 6]]]], [[[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]]]], [[[8, 7, 3], [3, 8, 4]], [2, 2, [[3, 7, 8], [4, 8, 3]]]]], [[[[0], [1], [0]], [5, 5, [[0, 1, 0]]]], [[[3, 0, 4]], [1, 1, [[3, 0, 4]]]], [[[6], [5]], [6, 8, [[5, 6]]]], [[[2, 0, 5], [5, 0, 3], [2, 8, 2]], [1, 1, [[2, 0, 5], [5, 0, 3], [2, 8, 2]]]], [[[1, 1], [1, 0]], [3, 3, [[0, 1], [1, 1]]]], [[[0, 0], [0, 1]], [1, 1, [[0, 0], [0, 1]]]], [[[1]], [1, 1, [[1]]]], [[[1, 2, 5], [2, 4, 3]], [1, 1, [[1, 2, 5], [2, 4, 3]]]]]]
labels = ["regression: shape in comparison key", "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 fixtureActualExpectedOutcome
regression: shape in comparison key 0[1, 1, [[0, 0], [1, 1], [0, 1]]][5, 5, [[0, 1, 0], [0, 1, 1]]]Failed
repair trap 1[1, 1, [[0], [0], [0]]][5, 5, [[0, 0, 0]]]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, [[6], [0], [9]]][5, 5, [[6, 0, 9]]]Failed

SHA-256 / 8914073695ec2a9e4ca540b7320813a76eab4d44352c73ab8d928ed2ac7a9a59

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: (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 = (ow, oh, [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, 0], [1, 1], [0, 1]], [5, 5, [[0, 1, 0], [0, 1, 1]]]], [[[0], [0], [0]], [5, 5, [[0, 0, 0]]]], [[[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]]]], [[[6], [0], [9]], [5, 5, [[6, 0, 9]]]]], [[[[4], [3], [0]], [6, 8, [[0, 3, 4]]]], [[[4, 4]], [1, 1, [[4, 4]]]], [[[9], [0], [1]], [6, 8, [[1, 0, 9]]]], [[[1, 0], [1, 0]], [7, 7, [[0, 0], [1, 1]]]], [[[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]]]], [[[1, 1], [4, 4], [9, 1]], [8, 6, [[1, 4, 1], [1, 4, 9]]]]], [[[[5], [4]], [6, 8, [[4, 5]]]], [[[1, 1, 0], [1, 1, 0]], [2, 2, [[0, 1, 1], [0, 1, 1]]]], [[[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]]]], [[[6], [8]], [5, 5, [[6, 8]]]]], [[[[4], [5], [0]], [6, 8, [[0, 5, 4]]]], [[[3, 8, 5]], [1, 1, [[3, 8, 5]]]], [[[4], [4]], [5, 5, [[4, 4]]]], [[[6, 7, 5], [1, 9, 6], [6, 4, 6]], [8, 6, [[5, 6, 6], [7, 9, 4], [6, 1, 6]]]], [[[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]]]], [[[8, 7, 3], [3, 8, 4]], [2, 2, [[3, 7, 8], [4, 8, 3]]]]], [[[[0], [1], [0]], [5, 5, [[0, 1, 0]]]], [[[3, 0, 4]], [1, 1, [[3, 0, 4]]]], [[[6], [5]], [6, 8, [[5, 6]]]], [[[2, 0, 5], [5, 0, 3], [2, 8, 2]], [1, 1, [[2, 0, 5], [5, 0, 3], [2, 8, 2]]]], [[[1, 1], [1, 0]], [3, 3, [[0, 1], [1, 1]]]], [[[0, 0], [0, 1]], [1, 1, [[0, 0], [0, 1]]]], [[[1]], [1, 1, [[1]]]], [[[1, 2, 5], [2, 4, 3]], [1, 1, [[1, 2, 5], [2, 4, 3]]]]]]
labels = ["regression: shape in comparison key", "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 fixtureActualExpectedOutcome
regression: shape in comparison key 0[1, 1, [[0, 0], [1, 1], [0, 1]]][5, 5, [[0, 1, 0], [0, 1, 1]]]Failed
repair trap 1[1, 1, [[0], [0], [0]]][5, 5, [[0, 0, 0]]]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, [[6], [0], [9]]][5, 5, [[6, 0, 9]]]Failed

SHA-256 / d4fc5a81399f1ad4ee6b9c8b5d336fc6e12034f07fa19a4d255726862514dd96

3 / The verified repair

Exit 0
"""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: (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, 0], [1, 1], [0, 1]], [5, 5, [[0, 1, 0], [0, 1, 1]]]], [[[0], [0], [0]], [5, 5, [[0, 0, 0]]]], [[[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]]]], [[[6], [0], [9]], [5, 5, [[6, 0, 9]]]]], [[[[4], [3], [0]], [6, 8, [[0, 3, 4]]]], [[[4, 4]], [1, 1, [[4, 4]]]], [[[9], [0], [1]], [6, 8, [[1, 0, 9]]]], [[[1, 0], [1, 0]], [7, 7, [[0, 0], [1, 1]]]], [[[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]]]], [[[1, 1], [4, 4], [9, 1]], [8, 6, [[1, 4, 1], [1, 4, 9]]]]], [[[[5], [4]], [6, 8, [[4, 5]]]], [[[1, 1, 0], [1, 1, 0]], [2, 2, [[0, 1, 1], [0, 1, 1]]]], [[[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]]]], [[[6], [8]], [5, 5, [[6, 8]]]]], [[[[4], [5], [0]], [6, 8, [[0, 5, 4]]]], [[[3, 8, 5]], [1, 1, [[3, 8, 5]]]], [[[4], [4]], [5, 5, [[4, 4]]]], [[[6, 7, 5], [1, 9, 6], [6, 4, 6]], [8, 6, [[5, 6, 6], [7, 9, 4], [6, 1, 6]]]], [[[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]]]], [[[8, 7, 3], [3, 8, 4]], [2, 2, [[3, 7, 8], [4, 8, 3]]]]], [[[[0], [1], [0]], [5, 5, [[0, 1, 0]]]], [[[3, 0, 4]], [1, 1, [[3, 0, 4]]]], [[[6], [5]], [6, 8, [[5, 6]]]], [[[2, 0, 5], [5, 0, 3], [2, 8, 2]], [1, 1, [[2, 0, 5], [5, 0, 3], [2, 8, 2]]]], [[[1, 1], [1, 0]], [3, 3, [[0, 1], [1, 1]]]], [[[0, 0], [0, 1]], [1, 1, [[0, 0], [0, 1]]]], [[[1]], [1, 1, [[1]]]], [[[1, 2, 5], [2, 4, 3]], [1, 1, [[1, 2, 5], [2, 4, 3]]]]]]
labels = ["regression: shape in comparison key", "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 fixtureActualExpectedOutcome
regression: shape in comparison key 0[5, 5, [[0, 1, 0], [0, 1, 1]]][5, 5, [[0, 1, 0], [0, 1, 1]]]Passed
repair trap 1[5, 5, [[0, 0, 0]]][5, 5, [[0, 0, 0]]]Passed
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[5, 5, [[6, 0, 9]]][5, 5, [[6, 0, 9]]]Passed

SHA-256 / 4e94f4d3a8a26b128c3a2edb381a681e68e4bcaf6289bf9fde0decf6d76a8ad3

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 2026-09-29T14:49:43.172353+00:00.

Case digest / bd4cc7bad97a04375cadedbac240f5d09857ace776cda4b7c2758f30af62b964