PDC-ViT : Source Camera Identification using Pixel Difference Convolution and Vision Transformer

Ahmed Bouridane; Fouad Khelifi; Noor Almaadeed; Omar Elharrouss; Somaya Al-Maadeed; Younes Akbari

REVIEW 4 major objections 6 minor 52 references

PDC-ViT : Source Camera Identification using Pixel Difference Convolution and Vision Transformer

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read PDC-ViT claims that pixel-difference convolution features fed into a Vision Transformer outperform prior source camera identification methods on five public benchmarks.

desk verdict Plausible PDC+ViT combination for source camera ID, but the evaluation is under-specified and the headline numbers are not yet trustworthy. read the letter →

arxiv 2501.16227 v1 pith:UMNZ7VA5 submitted 2025-01-27 cs.CV

Omar Elharrouss , Younes Akbari , Noor Almaadeed , Somaya Al-Maadeed , Fouad Khelifi , Ahmed Bouridane This is my paper · ORCID

classification cs.CV

keywords sourcecameraidentificationPixelDifferenceConvolutionVisionTransformerangularradialmultimediaforensicsmodeldeeplearning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes PDC-ViT, a source camera identification method that replaces content-based feature extraction with Pixel Difference Convolution (PDC), computing angular and radial differences between neighboring pixels, and feeds those features into a Vision Transformer for classification. The claim is that this combination captures sensor-specific pixel-level variations better than conventional CNN backbones, and the reported results support that: 94.30% on Vision, 84.06% on Daxing, 94.22% on Socrates, 92.29% on QUFVD, and 74.77% on Video-ACID, each beating previously published methods on those benchmarks. A sympathetic reader would care because source camera identification is used in digital forensics to trace images and videos to the device that captured them, and the paper argues that pixel-difference features plus attention give a content-independent, high-accuracy route to that trace.

What carries the argument

The load-bearing mechanism is Pixel Difference Convolution, which replaces the vanilla convolution sum with weighted differences between paired neighboring pixels: $Y = \sum_{(x_i,x_i') \in P} w_i \,(x_i - x_i')$. Angular PDC uses 3x3 kernels that follow a circular ordering of pixel pairs; Radial PDC uses 5x5 kernels that pair a center pixel with radially arranged neighbors. These PDC feature maps are concatenated and embedded as 64x64 patches into a Vision Transformer with six encoder layers, sixteen attention heads, and embedding dimension 1024. The PDC stage is what suppresses scene content and exposes sensor-specific micro-patterns; the ViT stage is what classifies those patterns globally. The paper's comparison of APDC-only, RPDC-only, and combined variants isolates the contribution of each difference geometry.

What would settle it

Re-run the evaluation with a strict camera-disjoint or video-disjoint split, training on some cameras or videos and testing only on cameras or videos never seen in training, and compare the accuracy. If PDC-ViT's margin over baselines collapses or drops substantially, the claimed generalization to source cameras is an artifact of same-video frame overlap.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that source camera identity is best learned from pixel-difference statistics rather than from scene content. The proposed PDC-ViT computes Angular PDC with 3x3 kernels and Radial PDC with 5x5 kernels, concatenates their feature maps, and feeds the resulting PDC features as patches to a Vision Transformer. Across five public datasets, the authors report accuracy of 94.30% (Vision), 84.06% (Daxing), 94.22% (Socrates), 92.29% (QUFVD), and 74.77% (Video-ACID with added devices), exceeding the best prior results they compare against on each benchmark. They also report that combining angular and radial branches outperforms either branch alone, with 92.29% versus 85.47% and 83.36% on QUFVD, and that PDC feature clusters are more separated in t-SNE visualization than ResNet101 features.

Load-bearing premise

The reported accuracy depends on the 80/20 train/test split being free of frame-level leakage: frames from the same video or recording session must not appear in both training and testing, because the model could then recognize a particular video's noise pattern rather than the camera's fingerprint.

Editorial extensions

If this is right

If the reported numbers hold, pixel-difference features plus a Vision Transformer are enough to identify source cameras at state-of-the-art accuracy on closed-set benchmarks with up to 35 devices.
Combining angular and radial PDC branches improves over either branch alone, so the two difference geometries carry complementary identity information.
The method reaches competitive accuracy on both image datasets and video-frame datasets, suggesting the pixel-difference signal survives video compression and scene variation.
On Video-ACID, the overall false negative rate of 23.4% and false positive rate of 0.92% are lower than the PRNU-based baselines compared against, which would mean fewer missed identifications and fewer false attributions in forensic use.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A camera-disjoint evaluation is the natural next test: if the 94.30% Vision accuracy persists when training and test sets share no camera, the method would generalize to unseen devices of the same models; the paper's random 80/20 split does not establish this.
Because PDC suppresses scene content, the same backbone might transfer to manipulated or recompressed images more gracefully than content-based CNNs, but the paper does not test compression or anti-forensic manipulation.
The architecture suggests a cheap ablation probe: keeping the same ViT, replacing PDC with ordinary convolution at matched parameter count would isolate whether the pixel-difference operation itself, rather than the larger effective receptive field, drives the gain.
The reported t-SNE separation of PDC features against ResNet101 features hints that an open-set or verification variant of PDC-ViT could work, but the paper only evaluates closed-set classification.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's the quick take: this paper combines Pixel Difference Convolution (angular and radial) with a Vision Transformer for closed-set source camera identification. That's a genuine application-level novelty—no one else feeds PDC features into a ViT for this task—and the method is plausible in principle: PDC emphasizes local pixel differences that carry sensor artifacts, and ViT can capture long-range dependencies. The paper also evaluates on five datasets and compares against a wide range of baselines, with confusion matrices and t-SNE plots. That is real work.

But the evidence is not as solid as the claims. The central problem is the split protocol. Section 4.3 says test images are 'selected randomly from each set of cameras.' That does not tell us whether frames from the same video can appear in both training and test. For Vision, Daxing, QUFVD, and Video-ACID, frames extracted from a shared recording share scene content, lighting, compression, and temporal noise. If any video spans both partitions, the classifier can separate cameras by video-specific cues, and accuracy is inflated. The paper simply never confirms a video-disjoint or camera-disjoint split, and the stress-test note is right that this is load-bearing. The comparisons are also heterogeneous: Table 2 mixes results from 11, 28, and 35 devices at different resolutions, so the claimed margins over prior work are not established under a controlled protocol.

There are also smaller but telling inconsistencies. The Daxing accuracy appears as 82.81 in Section 4.4 and 84.06 in Table 3. In Section 4.6 the text says RPDC-ViT scores 85.47 and APDC-ViT scores 83.36, but Table 5 lists them the other way around. No error bars or repeated runs are reported, and the code is only promised 'when the paper is accepted.'

None of this is fatal to the idea. Pixel-difference features are a reasonable choice for this task, and the pipeline is simple and easy to reproduce once the split is pinned down. But right now the headline numbers are unverified, not refuted. A clean video-disjoint evaluation with controlled baselines and variance estimates could settle it. I would not cite this in its current form, and I would be cautious about building on the numbers. It does deserve a serious referee, though: the topic is relevant, the combination is novel enough, and the flaws are fixable.

Referee Report

4 major / 6 minor

Summary. The paper proposes PDC-ViT, a source camera identification method that computes Angular and Radial Pixel Difference Convolution features from input images and feeds them to a Vision Transformer for classification. The authors report closed-set identification accuracies of 94.30% on Vision, 84.06% on Daxing, 94.22% on Socrates, 92.29% on QUFVD, and 74.77% on Video-ACID, and they compare these numbers with previously published results on the same datasets to claim state-of-the-art performance. The manuscript also provides confusion matrices and t-SNE visualizations for some datasets.

Significance. If the reported results survive a clean evaluation, the idea of using PDC as a feature extractor for ViT-based source camera identification is a reasonable engineering contribution that avoids explicit PRNU estimation and could be useful for forensic applications. The paper is honest about its limitations (Section 5) and uses public datasets, which makes the claims checkable in principle. However, the current validation does not yet establish the central 'superiority' claim: the split protocol may allow frame-level leakage, the baselines are compared under heterogeneous protocols, and several numbers are internally inconsistent.

major comments (4)

[§4.1, §4.3] The evaluation does not confirm that the training/validation/test split is disjoint at the video or device level. Section 4.1 states only that 'we used 80% of each dataset for Training/validation and 20% for testing,' and Section 4.3 says 'the images are selected randomly from each set of cameras'; for video-based datasets such as Vision, Daxing, QUFVD, and Video-ACID, this procedure can place frames from the same video or recording session in both training and test, allowing the model to exploit video-specific content and inflate accuracy. A video-disjoint (or at least session-disjoint) split is necessary to support the claim that PDC-ViT generalizes to new captures from known cameras; please describe the exact partition rule and rerun the experiments under a disjoint partition.
[Tables 2–5] The comparisons against state-of-the-art methods are not controlled. In Table 2, baseline accuracies are cited for different numbers of devices (11, 28, or 35), different input resolutions (32x32 to 480x800), and different training protocols; Table 4 gives no protocol information for the baselines; and Table 5 mixes resolutions and does not specify the device subset used for evaluation. Since accuracy in this task depends strongly on class count, image size, and split, the claimed margins over prior work are not established by the presented numbers. Please either re-run the baselines under identical conditions or clearly justify why cross-paper comparisons with differing protocols are valid.
[§4.4, §4.6, Table 3, Table 5] Several reported numbers are internally inconsistent. Section 4.4 reports a Daxing accuracy of 82.81 but Table 3 reports 84.06 for the same setting; Section 4.6 says PDC-ViT, RPDC-ViT, and APDC-ViT achieved 92.29%, 85.47%, and 83.36%, respectively, while Table 5 lists RPDC-ViT as 83.36 and APDC-ViT as 85.47, reversing the two variants. These contradictions need to be resolved and the correct table/text values verified before the accuracy claims can be assessed.
[§4.4–§4.7] All reported accuracies are single-run numbers without error bars or repeated trials, and the stability claim in Section 4.7 is based on one run. Given that some margins over the second-best method are small (e.g., 1.91% in Table 4), statistical significance is unknown. Please provide mean and standard deviation over multiple training seeds and, for video datasets, per-video aggregated metrics to show that the improvement is not due to a particular train/test draw.

minor comments (6)

[§3.1] In the sentence 'the use of pixel difference convolution exploding the angular and radial differences ... using 3 × 3 kernels for Angular PDC (APDC) and 5 × 5 kernels for Radial PDC (APDC)', the second occurrence of '(APDC)' should be '(RPDC)'.
[§4.2] The sentence 'In the case of the iPhone 6S (Plus), 23 different smartphone models are available' is unclear; it seems to describe the Daxing dataset but the wording should be revised.
[§4.7] The text uses 'FNTs' where 'FNRs' is intended, and the abbreviation 'Video W A PRNU' is never defined; please define it at first use (likely 'weighted averaging PRNU' from reference [27]).
[Figure 4] The caption of Figure 4 lists 'Vision dataset' in its title while the subfigures and text refer to QUFVD; please correct the caption to match the content.
[Table 6] The 'Overall' row in Table 6 does not specify whether the FNR and FPR are macro-averaged over devices or micro-averaged over all test samples; please clarify the averaging procedure.
[Data availability] The code availability statement says code 'will be published when the paper is accepted'; since the evaluation protocol is a central issue, releasing the exact data split would substantially strengthen reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

Empirical architecture evaluation; no derivation reduces to its own inputs.

full rationale

PDC-ViT is an empirical architecture paper: PDC feature extraction (Eqs. 2-3, based on prior external work [28-30]) is combined with a standard ViT classifier [33], and headline accuracies are measured on public datasets under a supervised split. No closed-form result is derived from a formula that already contains the answer, and no fitted parameter is renamed as a prediction. The only self-referential elements are dataset [7] and comparison baselines [27], [37] authored by the same group, but these are used as external benchmarks, not to set constants or to justify the method's correctness; the comparisons would not change if the baselines were replaced by independent implementations. Reported inconsistencies (Daxing 82.81 vs 84.06; APDC/RPDC accuracy reversal in Sec 4.6) and the unverified random frame-level split are validity/reproducibility concerns, not circularity. There is no self-citation chain that forces the outcome, so the circularity score is 0.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The central accuracy claims rest on three domain assumptions: that pixel-difference features capture camera-specific noise, that the 80/20 split avoids frame-level leakage, and that cross-paper accuracy comparisons are meaningful despite different resolutions and device counts. No invented entities are introduced. The only free quantities are hand-set hyperparameters and the fixed 224x224 crop.

free parameters (5)

learning rate = 0.00003
Chosen by hand with no schedule or sensitivity analysis; the Adam optimizer is used at this fixed rate.
input crop size = 224 x 224
All datasets are cropped to 224x224; the Daxing comparisons show that input resolution strongly affects accuracy, so this hand choice influences the reported results.
ViT patch size = 64 x 64
Set for the Vision Transformer input; no ablation on patch size is reported.
ViT hyperparameters = dim 1024, depth 6, heads 16, mlp_dim 2048, dropout 0.1
Hand-selected architecture hyperparameters with no ablation or sensitivity analysis.
number of PDC blocks per type = 2 blocks each for APDC and RPDC
The architecture depth is chosen by hand, and no ablation shows this configuration is optimal.

assumptions (3)

domain assumption PDC features computed from angular and radial pixel differences retain camera-specific sensor noise needed for identification.
The paper assumes pixel-difference statistics are discriminative across cameras; this is plausible but not proven in isolation against other feature extractors.
domain assumption The 80/20 train-validation-test split avoids frame-level leakage and respects each dataset's intended evaluation structure.
Section 4.1 states the split but does not describe whether frames from the same video or camera session are kept in one partition; the claimed accuracies depend on this.
domain assumption Reported accuracies of competing methods in Tables 2-5 are directly comparable despite differing image sizes, device counts, and protocols.
The comparison tables mix resolutions and dataset subsets from different papers without recalibration, so the performance gap is not a controlled comparison.

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Cite this review

Pith. "Pith review of PDC-ViT : Source Camera Identification using Pixel Difference Convolution and Vision Transformer." pith.science (2026). https://pith.science/paper/UMNZ7VA5

@misc{pith2026250116227,
  author       = {Pith},
  title        = {Pith review of: PDC-ViT : Source Camera Identification using Pixel Difference Convolution and Vision Transformer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UMNZ7VA5}},
  note         = {Machine review of arXiv:2501.16227}
}

read the original abstract

Source camera identification has emerged as a vital solution to unlock incidents involving critical cases like terrorism, violence, and other criminal activities. The ability to trace the origin of an image/video can aid law enforcement agencies in gathering evidence and constructing the timeline of events. Moreover, identifying the owner of a certain device narrows down the area of search in a criminal investigation where smartphone devices are involved. This paper proposes a new pixel-based method for source camera identification, integrating Pixel Difference Convolution (PDC) with a Vision Transformer network (ViT), and named PDC-ViT. While the PDC acts as the backbone for feature extraction by exploiting Angular PDC (APDC) and Radial PDC (RPDC). These techniques enhance the capability to capture subtle variations in pixel information, which are crucial for distinguishing between different source cameras. The second part of the methodology focuses on classification, which is based on a Vision Transformer network. Unlike traditional methods that utilize image patches directly for training the classification network, the proposed approach uniquely inputs PDC features into the Vision Transformer network. To demonstrate the effectiveness of the PDC-ViT approach, it has been assessed on five different datasets, which include various image contents and video scenes. The method has also been compared with state-of-the-art source camera identification methods. Experimental results demonstrate the effectiveness and superiority of the proposed system in terms of accuracy and robustness when compared to its competitors. For example, our proposed PDC-ViT has achieved an accuracy of 94.30%, 84%, 94.22% and 92.29% using the Vision dataset, Daxing dataset, Socrates dataset and QUFVD dataset, respectively.

Figures

Figures reproduced from arXiv: 2501.16227 by the authors.

**Figure 1.** Flowchart of the proposed network. In summary, the various methods proposed for camera model identification and source device identification offer significant advancements in digital forensics and multimedia analysis. Techniques such as dual-branch convolutional neural networks, hierarchical deep learning models, Discriminative Feature Projection, as well as the deployment of different CNN blocks have shown notable … view at source ↗

**Figure 2.** Selection of pixel pairs and convolution in Angular PDC. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Confusion matrices obtained of the evaluation using PDC-ViT on Vision and Daxing datasets. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: t-SNE visualization on QUFVD and Vision dataset [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Confusion matrices obtained of the evaluation using APDC-ViT, RPDC-ViT, and PDC-ViT [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 6.** Figure 6: Confusion matrix using PDC-ViT on Video-ACID dataset [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: FNR and FPR of of PDC-ViT and state-of-the-art methods on Video-ACID + Video WA PRNU [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

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Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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