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Title: Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations

Paper content:
Detection of photo manipulation relies on subtle statistical traces, notoriously removed by aggressive lossy compression employed online.
We demonstrate that end-to-end modeling of complex photo dissemination channels allows for codec optimization with explicit provenance objectives.
We design a lightweight trainable lossy image codec, that delivers competitive rate-distortion performance, on par with best hand-engineered alternatives, but has lower computational footprint on modern GPU-enabled platforms.
Our results show that significant improvements in manipulation detection accuracy are possible at fractional costs in bandwidth/storage.
Our codec improved the accuracy from 37% to 86% even at very low bit-rates, well below the practicality of JPEG (QF 20).
While the proposed approach can successfully facilitate pre-screening of photographs shared online, further research is needed to improve model generalization.
We observed that the fine-tuning procedure tends bias the DCN/FAN models towards the secondary image dataset, in our case the native camera output (NCO).
The baseline DCN was pre-trained on mixed natural images (MNI) with extensive augmentation, leading to competitive results on all test sets.
However, fine-tuning was performed on NCO only.
Characteristic pixel correlations, e.g., due to color interpolation, bias the codec and lead to occasional artifacts in MNIs (mostly in the clic test set; see Appendix B), and deterioration of the rate-distortion trade-off.
The problem is present regardless of λ c , which suggests issues with the fine-tuning protocol (data diversity) and not the forensic optimization objective.
We ran additional experiments by skipping photo acquisition and fine-tuning directly on MNI from the original training set (subset of 2,500 RGB images).
We observed the same behavior (see Appendix C), and the optimized codec was artifact-free on all test sets.
(Although, due to a smaller training set, the model loses some of its performance; cf. MNI results in Fig. A.6 .)
However, the FANs generalized well only to clic and kodak images.
The originally trained FANs generalized reasonably well to different NCO images (including images from other 3 cameras) but not to clic or kodak.
This confirms that existing forensics models are sensitive to data distribution, and that further work will be needed to establish more universal training protocols (see detailed discussion in Appendix D).
Short fine-tuning is known to help (Cozzolino et al., 2018) , and we leave this aspect for future work.
We are also planning to explore new transfer learning protocols (Li & Hoiem, 2017) .
Generalization should also consider other forensic tasks.
We optimized for manipulation detection, which serves as a building block for more complex problems, like processing history analysis or tampering localization (Korus, 2017; Mayer & Stamm, 2019; Wu et al., 2019; Marra et al., 2019a) .
However, additional pre-screening may also be needed, e.g., analysis of sensor fingerprints (Chen et al., 2008) , or identification of computer graphics or synthetic content (Marra et al., 2019b) .
Our study shows that lossy image codecs can be explicitly optimized to retain subtle low-level traces that are useful for photo manipulation detection.
Interestingly, simple inclusion of high frequencies in the signal is insufficient, and the models learns more complex frequency attenuation/amplification patterns.
This allows for reliable authentication even at very low bit-rates, where standard JPEG compression is no longer practical, e.g., at bit-rates around 0.4 bpp where our DCN codec with lowquality settings improved manipulation detection accuracy from 37% to 86%.
We believe the proposed approach is particularly valuable for online media platforms (e.g., Truepic, or Facebook), who need to pre-screen content upon reception, but need to aggressively optimize bandwidth/storage.
The standard soft quantization with a Gaussian kernel (Mentzer et al., 2018) works well for rounding to arbitrary integers, but leads to numerical issues for smaller codebooks.
Values significantly exceeding codebook endpoints have zero affinity to any of the entries, and collapse to the mean (i.e., ≈ 0 in our case; Fig. A.1a) .
Such issues can be addressed by increasing numerical precision, sacrificing accuracy (due to larger kernel bandwidth), or adding explicit conditional statements in the code.
The latter approach is inelegant and cumbersome in graph-based machine learning frameworks like Tensorflow.
We used a t-Student kernel instead and increased precision of the computation to 64-bits.
This doesn't solve the problem entirely, but successfully eliminated all issues that we came across in our experiments, and further improved our entropy estimation accuracy.
Fig.
A.2 shows entropy estimation error for Laplace-distributed random values, and different hyper-parameters of the kernels.
We observed the best results for a t-Student kernel with 50 degrees of freedom and bandwidth γ = 25 (marked in red).
This kernel is used in all subsequent experiments.
We experimented with different codebooks and entropy regularization strengths.
Fig.
A.3a shows how the quantized latent representation (QLR) changes with the size of the codebook.
The figures also compare the actual histogram with its soft estimate (equation 6).
We observed that the binary codebook is sub-optimal and significantly limits the achievable image quality, especially as the number of feature channels grows.
Adding more entries steadily improved quality and the codebook with M = 32 entires (values from -15 to 16) seemed to be the point of diminishing returns.
Our entropy-based regularization turned out to be very effective at shaping the QLR (Fig. A.3b ) and dispensed with the need to use other normalization techniques (e.g., GDN).
We used only a single scalar multiplication factor responsible for scaling the distribution.
All baseline and finetuned models use λ H = 250 (last column).
Fig.
A.4 visually compares the QLRs of our baseline low-quality codec (16 feature channels) with weak and strong regularization.

assistant: text: We learn an efficient lossy image codec that can be optimized to facilitate reliable photo manipulation detection at fractional cost in payload/quality and even at low bitrates.