Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: The paper describes a pipeline for image compression which allows to reliably detect specific manipulation patterns in compressed images.  The results show that it is possible to learn image compression that performs similarly to a modern image compression algorithm while in the same time is optimized to reveal specific kinds of manipulations. The authors build upon (Korus & Memon, 2019), but use a learnable codec instead of differentiable JPEG. 

The idea to regularize entropy of the latent representation of images is interesting. A method to train a well-performing image compression system which can also follow additional constraints (such as ability to reveal certain manipulations) is very valuable for practice. Unfortunately, there are already available trainable compression methods and the authors do not compare to these methods. However, in my opinion to detect manipulation in the image one should prove that visual content in some area of the image was significantly changed with respect to some original, while in the other parts of the image it was not changed. Otherwise it becomes impossible to distinguish in-camera filtering and secondary postprocessing. Basically, the authors present a method to detect whether a very particular configuration of some basic image processing filters (Gaussian blur, median filter, resampling)  was applied to the image. Therefore the particular problem formulation looks very artificial. 

With regards to the experiments in the paper, I was somewhat lost. Compare the Fig. 5 and the Fig. 8. In the Fig. 8, we see a big set of possible system configurations having different manipulation detection accuracy, image quality and compression performance.  In the Fig. 5, we see a compression efficiency-image quality dependency. However, it remains unclear how do the systems represented at these two graphs relate to each other, or, in the other words, what is he mapping between points of these graphs. Next, poor performance of JPEG manipulation detection by the proposed  network does not prove that JPEG manipulation cannot be detected, it just shows that the proposed architecture does not perform well in this problem. A comparative study which relates a new system to a current state of the art is required to claim that a proposed approach is better. Finally, SSIM is not a standard way to compute image quality. MS-SSIM and PSNR are also popular, and a user study is usually recommended to claim that some method generates images of better visual quality.

Summarizing, the authors do not provide a new best-performing image compression algorithm, and neither solve a problem of image manipulation detection, but show that it is possible to learn an image compression system with some additional constraints. I believe it is an interesting contribution, and I hope the authors can improve presentation of the experiments.
review_title: Official Blind Review #4

final_score: rating: 6: Weak Accept
review_content: This paper presents a learned image compression method that is able to be robust under a variety of tasks. The results aren't state of the art in terms of rate-distortion performance, but this paper has a very good analysis of the results, and has produced a very fast codec. In that sense, this is a very interesting paper that may lead to other fast methods (the other fast method they compared the runtime against - WaveOne never published a complete description).

This paper should be likely accepted, but the authors should town down the claims a bit. The results presented do NOT show that this method is better than the best hand engineered approach, despite what they claim. Even compared to BPG, which is NOT state of the art, the results are a mixed bag. 

We would like to point out to the authors that the VVC codec has shown much stronger performance than BPG, and similarly the AV1 codec has surpassed the performance of BPG. Moreover, even Pik has also surpassed the performance of BPG, so just showing stronger performance than BPG is not grounds to make the claim that this method is superior to hand engineered approaches.

Moreover, as I stated earlier, this method is not even better all the time, therefore weakening the claim.

On the positives:
- the paper fully describes the architecture, unlike WaveOne
- the runtime numbers are impressive (as far as I know, there is no faster published method)
- the authors consider applications other than compression performance (such as classification performance in forensic analysis)

On the negatives, which I highly suggest that the authors fix if this paper is to be taken seriously by the community:
- please be sure to explain that SSIM is computed in <RGB | grayscale>
- please be more explicit about which loss is used during training for distortion (i.e., "we use MSE for the training loss, but stop training when SSIM converges")
- please provide PSNR numbers for the method; and ideally MS-SSIM (in decibels) instead of PSNR
- please add other neural compression methods to the graphs 
- please clarify that you create a file and decode a file for each image used to create the graphs (very important topic), as opposed to using the estimated file size
- tone down the claims w.r.t. beating classical codecs
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: 
Summary of the paper
- This work proposes a new deep-learning-based method to replace the lossy compression techniques of images., jpg.
- The work investigates the role of codec and shows that the proposed complex photo dissemination channels optimizes the codec related traits on images.
- The method achieved much better performance in compressing images compared to practically used JPEG (QF-20)

I think the paper is well written and the experiment seems to support the author's argument. Unfortunately, this field is not overlapped to my research field, and it is hard for me to judge this paper.
review_title: Official Blind Review #772

Meta-review: The paper introduces a new image compression approach that preserves the patterns indicating image manipulation. The reviewers appreciate the idea and the method. Please take into account the suggestions of Reviewer1, when preparing the final version.
Decision: Accept (Poster)