{"id":"4f7a51e6-a4ad-42c6-9722-5157eb7c1bb4","arxiv_id":"2501.08604","paper_version":1,"verdict":"REJECT","confidence":"HIGH","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"Replacing DDIM inversion with EDICT in Gaussian Shading yields mixed watermark recovery results, contradicting the paper's claim of a statistically significant improvement.","lead":"Watermarking in diffusion models usually suffers from imprecise inversion. This paper pairs Gaussian Shading with EDICT, an exact inversion method, and finds a slight, mixed gain in watermark recovery, without solid statistical support.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Section 3's reverse process starts from x0=Encoder(I), y0=Duplicate(x0), while forward EDICT produced a non-identical coupled pair; exact inversion is therefore not performed, and the 'statistically significant improvement' claim is unsupported.","rationale":"Good-faith reading. The paper is a straightforward integration of EDICT into Gaussian Shading. What it claims is that EDICT's exact inversion improves watermark recovery. The single most load-bearing condition is that the inversion actually runs EDICT on the same coupled latents that were used forward. That condition is not met: the algorithm recovers the initial watermark latent by encoding the decoded image and duplicating it, which is neither the forward x0 nor the coupled y0. The reader's weakest assumption point is correct and hits the same issue. I also note secondary empirical problems: the only table reported in the text shows TPR decreasing for ColorJitter and SPNoise and unchanged elsewhere, no statistical tests; the phrase 'statistically significant' is asserted but not computed. These would independently prevent acceptance. The paper has no code or formal verification, and the limitation section admits 2x slowdown, but these are secondary. The proposed test stores the true pair and compares reverse initializations; it isolates whether the lossy encoder/duplicate-y0 issue matters. Since the central claim requires exactness and the implemented pipeline is not exact, the reader's REJECT stands.","tokens_in":4958,"tokens_out":5036,"duration_ms":52928,"concrete_test":"Store the true forward EDICT latent pair (x0, y0) at the end of denoising for 1000 watermarked images. For each image, run the paper's reverse process exactly as written, starting from x0_hat=Encoder(I), y0_hat=Duplicate(x0_hat), and compare watermark bit accuracy/TPR against reverse starting from the true stored (x0, y0). If both recover the watermark equally well, the lossy-encoder/duplicate-y0 objection does not explain the results; if true-pair inversion is materially better, the paper's reported numbers come from an approximate, non-exact pipeline and the central exactness claim fails. Report mean differences and bootstrap 95% CIs for both conditions.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing mechanism is that EDICT's exact inversion preserves the watermark noise latent. Section 3's Algorithm applies EDICT reverse after initializing x0=Encoder(I), y0=Duplicate(x0). This is not the latent trajectory generated in the forward pass. Forward EDICT denoising from the watermarked pair (xT, yT) yields a coupled pair (x0, y0) with y0 != x0, and only x0 is passed to Decoder to form I. The VAE encoder is lossy and is not the inverse of the decoder, so the encoded latent is not the forward x0; setting y0 equal to the encoded x0 also cannot reproduce the true forward y0. Thus the reverse coupling does not invert the actual forward EDICT steps. The exact-inversion property of EDICT applies to the coupled state pair, not to this re-initialized approximate pair, and any reconstruction error propagates through all T reverse steps. The paper provides no analysis or experiment isolating this error; it simply asserts 'a more precise inversion process' and 'significantly less distorted watermark extraction.' The empirical tables also fail to support the abstract's 'statistically significant improvement': TPR is unchanged for six of nine manipulations and decreases for two (ColorJitter and SPNoise), and no confidence intervals, p-values, seeds, or multiple-run statistics are reported. Because the claimed gain is attributed to exact inversion but the implemented pipeline is not exact, the central claim is not established.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes combining the Gaussian Shading watermarking method with EDICT, a diffusion inversion technique that maintains two coupled latents. In the forward pass, a watermarked latent is duplicated and denoised with EDICT's alternating coupled updates; one of the resulting latents is decoded to an image. In the reverse pass, the image is re-encoded, duplicated, and processed by EDICT's reverse steps to obtain a noisy latent from which the watermark is recovered. The authors evaluate the approach on Stable Diffusion 2.1 with 1000 images under nine image manipulations, comparing true positive rate, traceability, and bit accuracy with and without EDICT. The abstract claims a slight yet statistically significant improvement in watermark recovery fidelity.","tokens_in":5289,"tokens_out":6978,"duration_ms":60364,"significance":"If the central claim were valid, the paper would offer a training-free enhancement to a published watermarking method and would be the first to couple EDICT with Gaussian Shading. The paper provides pseudocode, uses a standard public model and evaluation protocol, and honestly reports the 2x computational overhead. However, the central claim is not established. The reverse process described in Section 3 does not perform EDICT's exact inversion because it initializes from Encoder(I) rather than the forward coupled latents, and the VAE encoder is lossy. The reported results are mixed and no significance testing is provided, contradicting the abstract's assertion. Thus the paper's contribution, as presented, is not substantiated.","major_comments":[{"comment":"The exact inversion property of EDICT applies to the specific coupled trajectory (x_T, y_T) produced in the forward pass. In the reverse algorithm, x0 is set to Encoder(I) and y0 to Duplicate(x0). Since the forward pass ends with y0 ≠ x0 in general, and since the VAE encoder is lossy and not the exact inverse of the decoder, the reverse process does not invert the forward EDICT trajectory. Consequently, the paper's assertion that the method ensures 'a more precise inversion process' and 'significantly less distorted watermark extraction' is not supported by EDICT's exactness guarantee. The authors need to supply an error analysis for this re-initialized reverse pass or demonstrate empirically that Encoder(I) is a sufficiently accurate proxy for the forward x0. Without this, the central mechanism of the paper is not implemented.","section":"Section 3, Algorithm (Reverse Process)"},{"comment":"The abstract claims a 'statistically significant improvement in watermark recovery fidelity,' but Section 4.1 reports TPR reductions for ColorJitter and SPNoise, an increase for GauNoise, and no change for the other six manipulations; traceability also decreases for ColorJitter. No confidence intervals, p-values, standard errors, or multiple-run statistics are provided. The descriptive results therefore contradict the abstract's claim, and the absence of significance testing makes the claim unverifiable. The authors should perform proper statistical tests across multiple seeds and report effect sizes, or revise the abstract to reflect the mixed empirical evidence.","section":"Abstract and Section 4.1"},{"comment":"The paper repeatedly invokes EDICT's 'exact inversion' property but never measures the accuracy of the reconstructed latent or image. Since the reverse pass starts from a lossy encoding, the reconstruction error is likely substantial, yet no quantitative assessment (e.g., LPIPS, MSE, or bit error rate of the recovered watermark latent) is reported. Without such a direct evaluation, the claim that EDICT provides 'exact' inversion in this watermarking pipeline is unverified.","section":"Section 4"}],"minor_comments":[{"comment":"In the forward process, step 2 reads 'yt ← -Duplicate(x1)'; this should reference x_t (or x_T) rather than x1, and the double assignment arrows are confusing.","section":"Section 3, Algorithm"},{"comment":"The capacity formula is unclear: the text first states 'the watermark capacity becomes l x c x h x w' and then introduces reductions by f_c and f_hw; please rewrite to give the final capacity formula explicitly.","section":"Section 2"},{"comment":"There are several typos: 'emply' should be 'employ', 'DDIMSolver' should be 'DDIM solver', and the sentence 'We sample 50 steps using DDIMSolver [5]. 50 steps of DDIM inversion was performed.' should be revised for grammatical correctness.","section":"Section 4"},{"comment":"The caption says performance 'improves or remains consistent' except for ColorJitter and Salt and Pepper noise, but the text also reports an improvement for GauNoise; please list all changes precisely to avoid inconsistency.","section":"Figure 2 caption"},{"comment":"References [4] and [5] are the same DDIM paper; consolidate them to avoid duplication.","section":"References"}],"recommendation":"reject","confidential_remarks":"The manuscript's central claim appears to rest on a misapplication of EDICT's exact inversion property, since the reverse pass begins from a lossy encoding rather than the forward coupled latents. The empirical results are mixed and lack statistical testing. I recommend reject. Note that the paper is short and appears to be an early-stage technical report; the lack of rigorous evaluation is a major concern."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nShort version: this paper replaces DDIM inversion with EDICT inside Gaussian Shading and claims a statistically significant improvement in watermark recovery. On the evidence in the paper, that claim is not supported, and the 'exact inversion' mechanism is not actually exact in the implemented pipeline. I would desk-reject it.\n\nWhat's good: the integration is described cleanly, the pseudocode is clear, and the experimental setup mostly follows prior work. The paper honestly reports in Section 4.1 that the TPR decreases for ColorJitter and Salt-and-Pepper noise, increases only for Gaussian noise, and is unchanged elsewhere. That is not a pattern that supports 'statistically significant improvement.' No error bars, p-values, seeds, or repeated runs are given, so the significance claim in the abstract is simply an assertion.\n\nThe deeper problem is the one the stress-test note flags. EDICT's exact-inversion property holds for the coupled latent pair generated during the forward pass. But in this paper's reverse process, you start from x0 = Encoder(I), where the VAE encoder is lossy and does not recover the original x0. You then set y0 = x0, which cannot reproduce the forward y0. So the 'exact inverse' trajectory is not the reverse of the forward trajectory; it is a fresh, approximate initialization. The paper gives no analysis of how large this reconstruction error is, and the mixed empirical results suggest it is not negligible. The claimed mechanism therefore does not do what the paper says.\n\nThe novelty is also modest: it is a swap of one published inversion module for another. That can be a useful engineering contribution if it works, but here it does not consistently, and the paper ships no code or data to let anyone check.\n\nWho is this for? Someone tracking watermarking in diffusion models might skim it, but the lack of evidence and the conceptual gap mean it should not advance. My recommendation: desk reject, or if you are feeling generous, send it back for a major revision with a request for proper statistics and an ablation that actually isolates the inversion error. I would not spend referee time on it as-is.","headline":"EDICT-in-Gaussian-Shading is a cleanly written engineering swap whose central significance claim is contradicted by its own results and whose 'exact inversion' setup is not actually exact.","tokens_in":5763,"tokens_out":2228,"would_cite":false,"duration_ms":21044,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Switching Gaussian Shading's inversion from DDIM to EDICT improves watermark recovery fidelity without retraining.","keywords":["Gaussian Shading","EDICT","diffusion inversion","latent diffusion watermarking","watermark recovery","DDIM inversion","coupled latent transformations"],"falsifier":"Encode a Stable Diffusion 2.1 generated image with the same VAE, measure the error between the original generation latent and the re-encoded latent, and check whether EDICT-recovered watermark bit accuracy drops in proportion to that error; if bit accuracy stays perfect even with a shifted latent, the exact-inversion explanation would be contradicted.","tokens_in":4773,"feed_emoji":"🖼️","tokens_out":5540,"duration_ms":50252,"temperature":0.7,"pith_summary":"This paper argues that replacing DDIM inversion with EDICT's exact coupled inversion inside Gaussian Shading improves watermark recovery from diffusion-generated images, without retraining. Gaussian Shading embeds a watermark in the initial noise of a latent diffusion model and recovers it by inverting the generation process; ordinary DDIM inversion is approximate, so the recovered watermark is distorted. The author's proposal duplicates the watermark-infused latent and denoises the two copies alternately, then reverses that coupled process to recover the original noise and thus the watermark. Experiments with Stable Diffusion 2.1 show a slight but statistically significant gain in recovery fidelity on most of nine image manipulations, though brightness and salt-and-pepper noise remain worse. The contribution is a training-free integration that improves inversion exactness for watermarking.","feed_headline":"EDICT inversion lifts watermark recovery in diffusion images","feed_subtitle":"Coupled-latent inversion raises watermark bit accuracy on 7 of 9 image manipulations, with no retraining.","key_machinery":"The central object is EDICT's reciprocal, alternating denoising of two coupled latents, $x_t$ and $y_t$, which are mixed with a factor $p \\in [0,1]$ at each step. The inverse direction algebraically inverts the mixing, so the watermark noise that entered the forward process can in principle be recovered exactly when the reverse starts from the true encoded latent. This mechanism carries the argument because the claimed improvement over Gaussian Shading is attributed entirely to replacing the approximate DDIM inversion with this exact coupled inversion.","core_discovery":"The central claim is that EDICT's reciprocal, alternating denoising of two coupled latents gives an exactly invertible diffusion path, and that substituting this path for DDIM inversion in Gaussian Shading yields a more faithful watermark extraction than the baseline. In the forward direction, the watermark is embedded in one noise latent, the latent is duplicated, and the coupled EDICT denoising produces an image while preserving the watermark information; in the reverse direction, the encoder's latent is duplicated and the coupled noising process is run backward, so one of the resulting noisy latents can be decoded into the embedded watermark. The reported result is higher bit accuracy in 7 of 9 manipulation settings, improved traceability under Gaussian noise, random drop, and salt-and-pepper noise, and detection performance that stays roughly unchanged.","pith_inferences":["The paper's exactness claim is conditional on the VAE encoder returning the same latent that generated the image, so for edited or re-encoded images the remaining watermark distortion may come from VAE lossiness rather than from inversion error.","The same coupled-latent substitution could be applied to other noise-latent watermarking schemes and to inversion-based image editing, where an exact inverse would reduce content drift.","A direct test of the mechanism would compare bit accuracy on latents taken straight from the generation pipeline against latents obtained by decoding and re-encoding the image: if EDICT's advantage shrinks after re-encoding, the residual distortion is from the autoencoder, not the inversion.","Since EDICT is 2x slower, the paper leaves open whether EDICT outperforms a compute-matched DDIM inversion run with twice the steps; that comparison would separate the benefit of exactness from the benefit of extra computation."],"forward_implications":["Watermark recovery fidelity improves for typical manipulations such as Gaussian blur, Gaussian noise, JPEG compression, median filter, random crop, random drop, and resize, with bit accuracy rising in 7 of 9 settings.","The approach requires no retraining or fine-tuning, so it can be inserted into existing Gaussian Shading pipelines and diffusion models that support EDICT.","Because EDICT's inversion is exact for the coupled latents, the main source of watermark distortion shifts from inversion error to encoder and manipulation losses, which should make detection thresholds more predictable on unmodified images.","The coupled scheme roughly doubles inference cost, making the method 2x slower than the baseline, a trade-off the paper explicitly acknowledges."],"supporting_citations":[{"why":"Supplies the Gaussian Shading embedding and recovery pipeline and the baseline numbers the paper compares against.","marker":"[7]"},{"why":"Supplies the EDICT coupled-latent inversion that replaces DDIM inversion in the proposed method.","marker":"[6]"},{"why":"Defines the DDIM inversion whose approximation error motivates the change.","marker":"[4]"},{"why":"Provides the Stable Diffusion 2.1 latent diffusion architecture used in the experiments.","marker":"[3]"},{"why":"Provides the DDIM solver used for the 50-step sampling and inversion in the evaluation.","marker":"[5]"}],"fun_headline_variants":["EDICT inversion ups watermark fidelity in diffusion images","Coupled latent inversion sharpens Gaussian Shading recovery","Exact inversion via EDICT enhances watermark extraction accuracy","EDICT + Gaussian Shading: better watermark recovery, no retraining"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The reverse process assumes the VAE encoder returns exactly the same latent that generated the image, so the paper's exactness claim depends on a losslessness that latent diffusion autoencoders generally do not provide; if encoding is lossy or the image is edited, the recovered watermark may still be distorted.","fun_headline_variants_meta":{"raw":{"variants":["EDICT inversion ups watermark fidelity in diffusion images","Coupled latent inversion sharpens Gaussian Shading recovery","Exact inversion via EDICT enhances watermark extraction accuracy","EDICT + Gaussian Shading: better watermark recovery, no retraining"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000376,"raw_usage":{"total_tokens":1995,"prompt_tokens":930,"completion_tokens":1065,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":546,"completion_tokens_details":{"reasoning_tokens":999}},"tokens_in":546,"tokens_out":1065,"duration_ms":9069,"temperature":1.0,"reasoning_tokens":999,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T20:20:49.804513+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Encode a Stable Diffusion 2.1 generated image with the same VAE, measure the error between the original generation latent and the re-encoded latent, and check whether EDICT-recovered watermark bit accuracy drops in proportion to that error; if bit accuracy stays perfect even with a shifted latent, the exact-inversion explanation would be contradicted.","supporting_citations":[],"review_version":1}