File size: 2,373 Bytes
9bd3ee0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | """Provider tests for ELA (Error Level Analysis)."""
from __future__ import annotations
import cv2
import numpy as np
import pytest
from config.settings import Settings
from pipeline.feature_extraction import PipelineOutput
from providers.forensics.ela import ELAProvider
@pytest.fixture
def ela_provider():
return ELAProvider(settings=Settings(environment="test", db_path=":memory:"))
@pytest.fixture
def pipeline_output(sample_image_bytes):
img = cv2.imdecode(np.frombuffer(sample_image_bytes, np.uint8), cv2.IMREAD_COLOR)
return PipelineOutput(
image=img, image_hash="h", width=img.shape[1], height=img.shape[0], source="bytes",
original_bytes=sample_image_bytes, original_format=".jpg",
)
class TestELAProvider:
def test_name(self, ela_provider):
assert ela_provider.name == "ela"
def test_capability(self, ela_provider):
from models.providers import ProviderCapability
assert ela_provider.capability == ProviderCapability.FORENSICS
def test_is_available(self, ela_provider):
assert ela_provider.is_available() is True
def test_execute_returns_metrics(self, ela_provider, pipeline_output):
result = ela_provider.execute(pipeline_output)
assert result.success is True
n = result.normalized
assert "ela_score" in n
assert "manipulation_indicators" in n
assert "details" in n
assert 0.0 <= n["ela_score"] <= 1.0
assert "mean_diff" in n["details"]
assert "max_diff" in n["details"]
def test_ela_score_in_range(self, ela_provider, pipeline_output):
result = ela_provider.execute(pipeline_output)
assert 0.0 <= result.normalized["ela_score"] <= 1.0
def test_synthetic_image_low_ela(self, ela_provider, sample_image_bytes):
# A synthetic JPEG re-encoded at quality 90 should have low ELA
# (it was never manipulated)
img = cv2.imdecode(np.frombuffer(sample_image_bytes, np.uint8), cv2.IMREAD_COLOR)
po = PipelineOutput(
image=img, image_hash="h", width=img.shape[1], height=img.shape[0],
source="bytes", original_bytes=sample_image_bytes, original_format=".jpg",
)
result = ela_provider.execute(po)
# ELA score should be relatively low for a clean image
assert result.normalized["ela_score"] < 0.8
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