Double Quantization analysis detects the traces left by
consecutive JPEG compressions on an image. When a spliced region from one image is inserted into another, if
the
compression histories of the two images differ, the discrepancy may be detected by this algorithm. A typical
case of forgery that is detectable by this algorithm is when an item is taken from an image of high quality
(or
an uncompressed image, or an image that had its past JPEG traces destroyed by scaling/filtering) and placed
in
an image of lower quality. If the resulting spliced image is then saved as at a high quality, this should
result
in a successful detection. In the output map, red values (=1) correspond to high probability of a single
compression for the corresponding block, while low values (=0) correspond to low probability of single
compression. Localized red areas in an otherwise blue image are very likely to contain splices. Images with
non-localized high values and values in the range (0.2-0.8) (green/yellow/orange) should not be taken into
account.
Jlpt N1 Old Question Instant
Jlpt N1 Old Question Instant
For more details, see: Lin, Zhouchen, Junfeng He, Xiaoou Tang, and Chi-Keung Tang. "Fast,
automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis." Pattern Recognition
42,
no. 11 (2009): 2492-2501.
Kenji shuffled through the cardboard box in his closet, the scent of mothballs and forgotten time wafting up. He was looking for an old savings account passbook. Instead, his fingers brushed against a creased, yellowed envelope. On the front, in fading ink, was a single word: “Sensei.”
Then the owner, an elderly man named Mr. Yamamoto—whom everyone called Sensei —had dismissed the police. He had looked at Kenji, not with anger, but with a tired disappointment that was far worse. "You taught my students kanji," Sensei had said quietly. "You taught them that 'trust' is written with the radical for 'person' and the word for 'speech.' And yet, you chose to erase the person from your own word."
Last week, he had looked up the old cram school. It was a convenience store now. A quick search of Mr. Yamamoto’s name led to a funeral home’s online memorial registry. Sensei had passed away five years ago.
Kenji had nodded, trembling. He worked three jobs, finished his degree, and landed a mediocre but stable job at a logistics firm. He saved. He married. His daughter was born. Life, as it does, accreted—layers of routine, small compromises, and deferred intentions. The ¥300,000 sat in a separate account for years. But the card … the card became a silent accusation.
He didn’t need to open it. He already knew what was inside: a receipt for ¥300,000, dated August 12, 1998. And a blank postcard.
He never sent it.
Kenji stared at the receipt. The debt was monetary, yes. But the real debt—the one he could never repay—was the opportunity to look Sensei in the eye and say, “I am no longer the man who stole.”
Why? That was the question that haunted him as he held the envelope now, retired, his daughter grown. At first, it was poverty. Then, pride—he wanted to send ¥500,000, to prove he was more than his mistake. Then, the shame of the delay itself. Each passing year made the blank card heavier. A postcard that should have taken a year became a decade. A decade became a lifetime.
Sensei paid back the missing money from his own pension. He gave Kenji a receipt for the amount, and a blank postcard. "When you can repay the debt," he said, "write the date and the amount on this card. Then send it. Not before."
Kenji turned and walked home. For the first time in twenty-five years, he did not feel the weight of a card in his pocket. He only felt the quiet, bitter grace of a letter that would never arrive.
The sound of the letter hitting the bottom echoed for a second, then was gone.
He took out a pen. Slowly, deliberately, he wrote on the blank postcard:
The Unpaid Debt
He addressed it to the old cram school’s address, knowing it would return as undeliverable. He sealed the envelope. Then he walked to the post office, bought a stamp, and dropped it into the red mailbox.
JPEG blocking artifact inconsistencies are traces left
when
tampering JPEG images by splicing, copy-moving or inpainting. JPEG compression is based on a non-overlapping
grid of adjacent blocks of 8×8 pixels. Any part of an image that has undergone at least one JPEG compression
carries a blocking trace of this dimension, and its presence is stronger at lower JPEG qualities. When
performing any forgery, it is highly likely that the 8×8 grid of the spliced or moved area will misalign
with
the rest of the image and leave a visible trace. The outputs of this algorithm are often noisy, and are
occasionally activated by high-variance image content, so an investigator should look for inconsistencies in
regions that should be uniform. In the third ȐDetectionsȑ example, the high values around the keyboard keys
are
to be expected due to the sharp edges. The discontinuities in the areas around the lower post-it, the upper
badge and the upper marker, on the other hand, cannot be attributed to image content, as they occur in the
middle of the (uniform) table surface. Thus, they have to be attributed to alterations of the image content.
Jlpt N1 Old Question Instant
Jlpt N1 Old Question Instant
For more details, see: Li, Weihai, Yuan Yuan, and Nenghai Yu. "Passive detection of doctored
JPEG
image via block artifact grid extraction." Signal Processing 89, no. 9 (2009): 1821-1829.
Error Level Analysis is based on a technique very
similar
to JPEG Ghosts, that is the subtraction of a recompressed JPEG version of the suspect image from the image
itself. In contrast to JPEG Ghosts, only a single version of the image is subtracted -in our case, of
quality
75. Furthermore, while the output of JPEG Ghosts is normalized and filtered to enhance local effects, ELA
output
is returned to the user as-is. The assumption is that, when subtracting a recompressed version of the image
from
itself, regions that have undergone fewer (or less disruptive, higher-quality) compressions will yield a
higher
residual. When interpreted by an analyst, areas of interest are those that return higher values than other
similar parts of the image. It is important to remember that only similar regions should be compared, i.e.
edges
should be compared to edges, and uniform regions should be compared to uniform regions.
Jlpt N1 Old Question Instant
Jlpt N1 Old Question Instant
For more details, see: http://fotoforensics.com/tutorial-ela.php
Median Noise Residuals operate based on the observation
that different images feature different high-frequency noise patterns. To isolate noise, we apply median
filtering on the image and then subtract the filtered result from the original image. As the median-filtered
image contains the low-frequency content of the image, the residue will contain the high-frequency content.
The
output maps should be interpreted by a rationale similar to Error Level Analysis, i.e. if regions of similar
content feature different intensity residue, it is likely that the region originates from a different image
source. As noise is generally an unreliable estimator of tampering, this algorithm should best be used to
confirm the output of other descriptors, rather than as an independent detector.
Jlpt N1 Old Question Instant
Jlpt N1 Old Question Instant
For more details, see: https://29a.ch/2015/08/21/noise-analysis-for-image-forensics
High-frequency noise patterns can be used for splicing
detection, as the local noise variance of an image is often unique and distinctive. This method detects the
local variance of high-frequency information on an image. In the resulting output maps, whether values are
high
or low is irrelevant. What is significant is the presence of localized consistent differences in noise
variance
values. Since high-frequency noise can be affected by the image content, comparisons should be made between
visually similar areas (e.g. edges to edges, smooth areas to smooth areas). Methods based on noise patterns
are
not particularly precise, and unless extremely clear patterns appear, this algorithm should be used in
conjunction with other detectors.
Jlpt N1 Old Question Instant
Jlpt N1 Old Question Instant
For more details, see: Mahdian, Babak, and Stanislav Saic. "Using noise inconsistencies for
blind
image forensics." Image and Vision Computing 27, no. 10 (2009): 1497-1503.
JPEG Blocking artifacts appear as a regular pattern of visible block boundaries in a JPEG
compressed image, as a result of the quantization of the coefficients and the independent
processing of the non-overlapping 8x8 blocks, during the DCT Transform. CAGI locates grid
alignment abnormalities in a JPEG compressed image bitmap, as an indicator of possible
forgery. Multiple grid positions are investigated in order to maximize a fitting function. Areas
of lower contribution are recognized as grid discontinuities (possible tampering). An image
segmentation step is introduced to differentiate between discontinuities produced by
tampering and those that are attributed to image content, clearing the output maps by
suppressing non-relevant activations. The higher readability of the maps comes with a cost
in the form of coarser-grained detection results, more so for low resolution images.
CAGI-Inversed accounts for tampering scenarios where the discontinuities appear as areas
of averagely higher contribution. The suppression of non-relevant activations is inversed
during the image segmentation step, and an alternative output maps is produced. The user
can then estimate the most appropriate output based on visual inspection.
JPEG Blocking artifacts appear as a regular pattern of visible block boundaries in a JPEG
compressed image, as a result of the quantization of the coefficients and the independent
processing of the non-overlapping 8x8 blocks, during the DCT Transform. CAGI locates grid
alignment abnormalities in a JPEG compressed image bitmap, as an indicator of possible
forgery. Multiple grid positions are investigated in order to maximize a fitting function. Areas
of lower contribution are recognized as grid discontinuities (possible tampering). An image
segmentation step is introduced to differentiate between discontinuities produced by
tampering and those that are attributed to image content, clearing the output maps by
suppressing non-relevant activations. The higher readability of the maps comes with a cost
in the form of coarser-grained detection results, more so for low resolution images.
CAGI-Inversed accounts for tampering scenarios where the discontinuities appear as areas
of averagely higher contribution. The suppression of non-relevant activations is inversed
during the image segmentation step, and an alternative output maps is produced. The user
can then estimate the most appropriate output based on visual inspection.
This is a deep learning approach on copy-move forgery detection. This approch aims to
highlight the copied and the correspoding original region with high values and the rest with low values.
The DCT algorithm operates on JPEG files. Tampered areas should appear as
high values on a low-valued background. Usually, if medium-valued regions are present, then no conclusion can be
made.
Mantra-Net is a deep learning approach for forgery manipulation detection. It
shows regions which it believes are forged. However, in the absence of automatic analysis of the results, visual
interpretation is needed to distinguish true detections from noise.
Each image carries invisible noise as a result of the image processing pipeline. Residual
noise is estimated and then used to extract features. Regions having different features than the rest of the
image are pointed as suspicious. Due to the normalization, there will always be at least one pixel at a high
value even on an authentic image. Furthermore, care should be taken analyzing saturated regions; when those are
not automatically masked by the algorithm they may be detected as forgeries even when they are authentic.
Due to the design of each particular camera, traces are left on every captured image. These traces are a sort of camera fingerprint. This method extracts this fingerprint and detects regions where this fingerprint is inconsistant with the rest of the image. Care should be taken analysing saturated regions, which tend to produce false positives when they are not automatically masked by the algorithm.
The OMGFuser algorithm detects regions of the image that have been visually altered. It provides a forgery localization mask, that highlights in red color the altered regions, while the authentic ones are highlighted in blue. Furthermore, it provides an overall forgery probability for the image, that indicates whether some of its parts have been forged. To achieve this, it combines the outputs of multiple AI-based filters that analyze different low-level traces of the image, using a novel deep-learning framework, thus greatly reducing the amount of false-positives. OMGFuser is currently in an experimental release stage.
The MM-Fusion algorithm detects regions of the image that have been visually altered. It provides a forgery localization mask, that highlights in red color the altered regions, while the authentic ones are highlighted in blue. To achieve this it combines the output of several noise-sensitive filters, in order to capture different traces left by the manipulation operations.
Related paper: Triaridis, K., & Mezaris, V. (2023). Exploring Multi-Modal Fusion for Image Manipulation Detection and Localization. arXiv preprint arXiv:2312.01790.
The development of this model was supported by the EU's Horizon 2020 research and innovation programme under grant agreement H2020-101021866 CRiTERIA.
The TruFor The algorithm detects regions of the image that have been visually altered. It provides a forgery localization mask, that highlights in red color the altered regions, while the authentic ones are highlighted in blue. Furthermore, it provides an overall forgery probability for the image, that indicates whether some parts have been forged. To achieve this it utilizes a novel AI-based filter, called Noiseprint++, that captures the detail of the noise pattern in different regions of the image.
Related paper: Guillaro, F., Cozzolino, D., Sud, A., Dufour, N., & Verdoliva, L. (2023). TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20606-20615).
OW-Fusion is a deep learning based approach that combines multiple forensic
filters and provides a overall localization. Tampered areas should appear as high values on a low-valued
background.
Kenji shuffled through the cardboard box in his closet, the scent of mothballs and forgotten time wafting up. He was looking for an old savings account passbook. Instead, his fingers brushed against a creased, yellowed envelope. On the front, in fading ink, was a single word: “Sensei.”
Then the owner, an elderly man named Mr. Yamamoto—whom everyone called Sensei —had dismissed the police. He had looked at Kenji, not with anger, but with a tired disappointment that was far worse. "You taught my students kanji," Sensei had said quietly. "You taught them that 'trust' is written with the radical for 'person' and the word for 'speech.' And yet, you chose to erase the person from your own word."
Last week, he had looked up the old cram school. It was a convenience store now. A quick search of Mr. Yamamoto’s name led to a funeral home’s online memorial registry. Sensei had passed away five years ago.
Kenji had nodded, trembling. He worked three jobs, finished his degree, and landed a mediocre but stable job at a logistics firm. He saved. He married. His daughter was born. Life, as it does, accreted—layers of routine, small compromises, and deferred intentions. The ¥300,000 sat in a separate account for years. But the card … the card became a silent accusation. jlpt n1 old question
He didn’t need to open it. He already knew what was inside: a receipt for ¥300,000, dated August 12, 1998. And a blank postcard.
He never sent it.
Kenji stared at the receipt. The debt was monetary, yes. But the real debt—the one he could never repay—was the opportunity to look Sensei in the eye and say, “I am no longer the man who stole.” Kenji shuffled through the cardboard box in his
Why? That was the question that haunted him as he held the envelope now, retired, his daughter grown. At first, it was poverty. Then, pride—he wanted to send ¥500,000, to prove he was more than his mistake. Then, the shame of the delay itself. Each passing year made the blank card heavier. A postcard that should have taken a year became a decade. A decade became a lifetime.
Sensei paid back the missing money from his own pension. He gave Kenji a receipt for the amount, and a blank postcard. "When you can repay the debt," he said, "write the date and the amount on this card. Then send it. Not before."
Kenji turned and walked home. For the first time in twenty-five years, he did not feel the weight of a card in his pocket. He only felt the quiet, bitter grace of a letter that would never arrive. On the front, in fading ink, was a single word: “Sensei
The sound of the letter hitting the bottom echoed for a second, then was gone.
He took out a pen. Slowly, deliberately, he wrote on the blank postcard:
The Unpaid Debt
He addressed it to the old cram school’s address, knowing it would return as undeliverable. He sealed the envelope. Then he walked to the post office, bought a stamp, and dropped it into the red mailbox.
