{"id":"9e3ca10c-6fb5-4c64-9dcd-9761f5aeb5ea","arxiv_id":"2505.08197","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":2.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A survey organizing diffusion-based visual watermarking into passive and proactive categories, with no new method, experiment, or quantitative benchmark.","lead":"This preprint reviews how diffusion models are used to embed, detect, and attack watermarks in AI-generated images, classifying methods into data-driven, sampling-driven, and adversarial proactive categories. A generalist reader interested in AI content provenance gets a map of current methods and open challenges, not new experimental results.","discovery_kind":"review","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Section 3.1's definition of passive watermarking contradicts the paper's own examples, and §2.2 misdescribes Recipe's method; the central taxonomy is not yet an accurate map.","rationale":"The reader identified the same risk via the Tree-Ring example: a method summary that contradicts the survey's stated definition of passive watermarking. My audit extends this concern: the problem is not a single wording slip but the absence of a consistent set of criteria for assigning methods to categories. Section 2.2 organizes by injection point (data-driven, sampling-driven, adversarial), while Section 3.1 reorganizes by a passive/proactive distinction defined as adding noise, which excludes several passive methods the survey itself lists. Adding to this is the internal contradiction around Recipe: Section 2.2 implies it belongs to full training with watermarks in the pretraining dataset, whereas Table 1 and Section 5.1 say it fine-tunes with trigger pairs. Because a survey's primary value is organizational accuracy, these inconsistencies support the reader's CONDITIONAL verdict: the paper is usable as a pointer list, but the taxonomy and Table 1 need correction before the map can be relied upon. I do not see a reason to move to reject, since the factual errors are local and fixable, and the broader literature coverage is still informative. I therefore keep the reader's verdict unchanged.","tokens_in":12909,"tokens_out":7422,"duration_ms":74334,"concrete_test":"Audit Table 1 against the original papers. For each row, extract the embedding mechanism (pixel-space perturbation, latent Fourier modification, decoder weight fine-tune, prompt trigger) and the presence of a payload decoder. Then test the two definitions: does §3.1's “adding small noise to images” hold for any of Tree-Ring, Stable Signature, or NaiveWM/FixedWM? Does §2.2's “full training… entire pretraining dataset” hold for Recipe? If either original paper contradicts the survey's Table 1 or Sections 3.1 or 2.2, the taxonomy section must be revised. A minimum check on the two cited works (Tree-Ring and Recipe) would settle the concern.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The survey's central claim is that the three-way taxonomy in §2.2 and Table 1 accurately organize current diffusion-model watermarking. The weakest load-bearing premise is that the category definitions consistently match the cited methods. This premise fails internally. §3.1 says “Passive watermarking involves adding small noise to images to enable copyright tracking, detection, and provenance verification.” But the passive methods the survey itself describes do not add small noise to images: Tree-Ring embeds rings in the Fourier coefficients of the initial latent noise vector, Stable Signature fine-tunes the LDM decoder, and NaiveWM/FixedWM insert keywords into prompts. The same section characterizes “proactive” methods as adversarial optimization, yet several of these (AdvDM, Glaze, Anti-DB) carry no payload and have no watermark decoder; the survey never states whether these are watermarks or unlearnable-example defenses. In addition, §2.2 associates data-driven “full training methods” with [Zhao et al., 2023a] and says such methods embed watermarks into the entire pretraining dataset, while §5.1 and Table 1 describe Recipe as fine-tuning a pre-trained diffusion model with trigger pairs. Because Section 4's challenge discussion is derived from these categories, the errors propagate to the survey's contributions. This is an internal inconsistency, not a difference with consensus.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper is a survey of visual watermarking techniques in the context of diffusion models. It proposes a three-way taxonomy of current methods: data-driven passive methods, sampling-driven passive methods, and adversarial proactive methods. It then reviews representative methods for image and model copyright protection, discusses effectiveness and robustness, identifies challenges such as computational cost, watermark-removal attacks, and multi-user attribution, and outlines applications in copyright protection, forensics, and privacy. The paper presents no new algorithms or experiments; its contribution is intended to be an organizational map of the emerging diffusion-watermarking literature.","tokens_in":13151,"tokens_out":6097,"duration_ms":57517,"significance":"If the taxonomy and method summaries were accurate, the survey would be a useful entry point to a rapidly growing and practically important area. The paper correctly states the DDPM/LDM training objectives in Eqs. (1)–(5), covers influential methods including Tree-Ring, Stable Signature, WOUAF, RingID, AdvDM, Glaze, and Anti-DB, and identifies real challenges such as denoising-based watermark removal (DiffPure/GrIDPure) and multi-key attribution. However, the paper's central value depends on the correctness of its categorization, and the categories are currently defined inconsistently with the cited methods. Because the challenge discussion in Section 4 is built on these categories, the inconsistencies propagate to the paper's main contributions. The issues are fixable within the scope of a survey, but they require substantive revision rather than copy editing.","major_comments":[{"comment":"Section 3.1 (repeated in Section 3.3 and Section 4.2) defines passive watermarking as 'adding small noise to images' and proactive watermarking as 'adversarial optimization to introduce noise into images.' These definitions are contradicted by the paper's own examples: Stable Signature fine-tunes the LDM decoder, Tree-Ring embeds rings in the Fourier coefficients of the initial latent noise vector, and NaiveWM/FixedWM inject keywords into prompts. Since Section 2.2's taxonomy and Table 1 are built on these definitions, the central classification of methods is not currently an accurate map of the literature.","section":"§3.1"},{"comment":"Section 2.2 assigns 'full training methods' the citation [Zhao et al., 2023a] and says such methods embed watermarks into the entire pretraining dataset, while Table 1 and Section 5.1 describe Recipe [Zhao et al., 2023a] as fine-tuning a pre-trained diffusion model with trigger pairs, which is the backdoor-attack subcategory that Section 2.2 attributes to [Liu et al., 2023]. The same method is thus placed in two different subcategories of the same taxonomy, making the taxonomy self-contradictory.","section":"§2.2, Table 1, §5.1"},{"comment":"Table 1 lists GrIDPure [Zhao et al., 2024b] under 'PW for Image' as a watermarking method whose description is 'discloses the vulnerability of existing watermarking methods.' GrIDPure is a watermark-removal/purification attack rather than a watermark-embedding method. Including it in the representative method list conflates attacks with defenses and undermines the claim that the table represents watermarking methods.","section":"Table 1"},{"comment":"Section 3.1 and Table 1 classify AdvDM, Glaze, Anti-DB, MetaCloak, and SIMAC as 'proactive watermarking,' but these methods carry no payload and define no watermark decoder; they produce adversarial examples that degrade downstream generation. The paper does not state whether it considers these methods to be watermarks or unlearnable-example defenses, and Section 4.3's discussion of multi-attribution assumes a decoder-based verification pipeline that these methods do not possess, so the challenge discussion does not apply uniformly to the methods listed under 'proactive watermarking.'","section":"§3.1, §4.3"},{"comment":"The Abstract claims that diffusion models 'enhance detection accuracy' and the Conclusion claims 'superior robustness and adaptability,' but the survey contains no experiments, benchmarks, or quantitative comparisons, and Sections 2–5 do not substantiate these comparative strength claims. Either the claims should be tempered to match what the survey actually shows, or the authors should cite specific quantitative evidence from the literature to support them.","section":"Abstract, Section 6"}],"minor_comments":[{"comment":"The Table 1 caption contains a typo: 'Representive' should be 'Representative'; similar typos appear in Section 3.2 ('diffsusion') and Section 4.3 ('indentification').","section":"Table 1"},{"comment":"The ROBIN reference [Huang et al., ] is missing its publication year and venue; the citation is incomplete and should be finished before publication.","section":"References"},{"comment":"Equation (2) defines α_t as a product over i=0 to t, but β_0 is not defined; the standard DDPM convention is a product over i=1 to t. The indexing should be corrected.","section":"Eq. (2)"},{"comment":"In Section 5.2, the sentence fragment 'like adversarial watermarking, can serve as a potential solution' lacks a grammatical subject and should be rewritten.","section":"§5.2"},{"comment":"The name of SIMAC is written as 'SimAC' in Section 3.2 and 'SIMAC' in Table 1; the authors should use a single consistent spelling.","section":"§3.2, Table 1"},{"comment":"The paper cites the SoK paper [Zhao et al., 2024a] but does not explicitly state how this survey differs from or complements that existing systematization; a short positioning paragraph would clarify the novel contribution.","section":"§1, §6"}],"recommendation":"major_revision","confidential_remarks":"This is an early-stage survey with useful coverage but several internal inconsistencies in its central taxonomy. The issues are organizational and definitional rather than indicative of fabrication or plagiarism. The authors should also position the work against the SoK paper they cite (Zhao et al. 2024a) to clarify what new organizational value the survey provides."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper is a survey, not a results paper, and it should be judged as one. As a first map of diffusion-watermarking methods, it has real value: the three-way split (data-driven passive, sampling-driven passive, adversarial proactive) captures something true about the field, Table 1 gives a compact entry point, and the DDPM/LDM equations in Section 2 are correct. The challenge list in Section 4 is sensible, and the authors do engage with a wide slice of the literature, including recent work like RingID and WOUAF. That is real credit, and it should not be buried.\n\nThe soft spots are mostly in the definitions, and they are not cosmetic. Section 3.1 says passive watermarking involves \"adding small noise to images,\" but the paper's own examples do not do that: Tree-Ring embeds rings in the Fourier coefficients of the initial latent, Stable Signature fine-tunes the decoder, and FixedWM/NaiveWM inject trigger prompts. That is an internal contradiction, not a dispute with consensus. The same sloppiness appears in Section 2.2, where data-driven full training is tied to [Zhao et al., 2023a] as if it embeds watermarks into the entire pretraining set, while Table 1 and Section 5.1 describe Recipe as fine-tuning a pre-trained model with trigger pairs. Those are different mechanisms, and the survey should not blur them. The proactive category also conflates true watermarks (with payloads and decoders) with unlearnable-example defenses like AdvDM and Glaze that carry no payload at all; the authors never state whether they think these are the same thing or different.\n\nThere are also a few unsupported evaluative claims—\"diffusion models enhance detection accuracy\" and \"superior robustness\" appear in the abstract and conclusion without citation or benchmark. And [Huang et al., ] is missing a year. None of this is fatal. The taxonomy survives if the definitions are corrected and the proactive category is split or explicitly defined. But as written, the central claim that this is an \"accurate map\" is not quite true.\n\nWho is this for? A newcomer who wants a quick list of method names and rough categories. That reader will get value, but they should be told to double-check the original papers. For a serious referee, I would send it—the scope is useful and the flaws are fixable—with a request for a careful revision of Sections 2.2 and 3.1, and a pass over the citations. I would not cite it in my own work until it is tightened.","headline":"A useful but sloppy survey map of diffusion watermarking, whose central taxonomy contradicts its own examples; fixable, worth peer review but not citable as authoritative.","tokens_in":13665,"tokens_out":1663,"would_cite":false,"duration_ms":18037,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A survey maps diffusion-model watermarking into three camps: data-driven passive, sampling-driven passive, and adversarial proactive.","keywords":["visual watermarking","diffusion models","text-to-image generation","copyright protection","adversarial examples","model provenance","watermark robustness","generative AI security"],"falsifier":"Inspect the primary sources behind the taxonomy: if a substantial share of the cited methods are not accurately described by the paper's category definitions, starting with whether Tree-Ring's watermark is in the initial latent's Fourier coefficients rather than in image pixels, then the taxonomy and the challenge list built on it do not hold as a map of the literature.","tokens_in":12716,"feed_emoji":"🖼️","tokens_out":8320,"duration_ms":74258,"temperature":0.7,"pith_summary":"This paper is a survey of visual watermarking in the era of diffusion models. Its central claim is that current diffusion-model watermarking methods fall into three categories: passive methods that embed watermarks into training data, passive methods that modify the sampling process or decoder so generated images carry a traceable mark, and adversarial proactive methods that perturb images so diffusion models cannot usefully copy or edit them. The paper argues diffusion models change the watermarking problem qualitatively because the same denoising machinery that creates images can also strip small watermark perturbations, pushing robust designs into frequency-domain, latent-space, and semantic-level strategies. It introduces no new watermark itself; its contribution is a map of the field plus a list of open problems: computational cost, resistance to diffusion-based purification, and multi-user attribution.","feed_headline":"Diffusion watermarks split into three camps, survey finds","feed_subtitle":"Here is how diffusion models both embed and erase watermarks.","key_machinery":"The organizing device is the three-category taxonomy anchored in the diffusion process itself. Equations (1)-(5) define the forward noising and learned denoising steps, and the taxonomy works by asking which step a method modifies: training data, initial latent, scheduler, decoder, or final image. The paper's exemplar for latent-level embedding is Tree-Ring, which places ring patterns in the Fourier space of the initial noise vector, putting the mark at the distributional level rather than the sample level; this is what lets a watermark survive spatial-domain denoising. The same pipeline map explains why adversarial proactive methods use iterative adversarial optimization: they must shift the image so that any downstream diffusion-based edit produces a visibly broken result.","core_discovery":"On the paper's own terms, the key organizing discovery is that the diffusion watermarking literature is structured by who is being protected and at which stage of the diffusion pipeline the mark enters. Data-driven passive methods protect data and model owners by hiding marks in training data or trigger pairs; sampling-driven passive methods modify the initial noise, the scheduler, or the decoder so that outputs carry a signature; adversarial proactive methods protect data owners by adding optimized perturbations that make images unrecognizable to unauthorized customization. The paper also claims this taxonomy exposes an asymmetry: diffusion models give defenders a flexible embedding mechanism, yet the same denoising capability gives attackers a purification mechanism, so watermark robustness has to be engineered against denoising and fine-tuning rather than only against conventional image processing.","pith_inferences":["If the taxonomy is accurate, the next wave of methods will hybridize the categories, for example by training an adversarial generator once to amortize the per-image cost of proactive marks, a direction the paper mentions as feasible but does not develop.","The paper's examples predict a testable ordering: latent-space and frequency-domain watermarks should survive diffusion purification better than pixel-space watermarks, and a systematic benchmark under purification and fine-tuning would confirm or refute this.","The passive/proactive split leaves a third notion implicit: watermarking as a differentiable part of the generative objective, where the model is trained to produce marked images by construction rather than marked afterwards.","If diffusion models keep improving at removing perturbations, the durable strategy implied by the paper is semantic-level modification, altering image content imperceptibly so the mark persists through regeneration, since high-frequency proactive marks are called out as ineffective."],"forward_implications":["A new method can be classified by answering two questions: who is protected (data owner or model owner) and where in the diffusion pipeline the mark is injected (data, sampling, decoder, or image).","Passive methods embedded during training or sampling add little or no per-image cost, while proactive adversarial methods pay for each sample with iterative optimization, so cost pressure should push adoption toward training-time and sampling-time designs.","Robustness evaluations should include diffusion-based purification and fine-tuning attacks, not just JPEG compression or cropping, because diffusion models can erase small perturbations while preserving image quality.","Binary model-level watermarking cannot identify individual users; multi-bit or multi-channel methods are required once attribution must name a specific user rather than a source model."],"supporting_citations":[{"why":"Recipe: defines the data-driven passive category by fine-tuning pre-trained diffusion models with trigger pairs.","marker":"[Zhao et al., 2023a]"},{"why":"NaiveWM/FixedWM: backdoor models with a keyword and target image, grounding the model-watermarking subcategory.","marker":"[Liu et al., 2023]"},{"why":"Stable Signature: fine-tunes an LDM decoder to embed a binary signature, the paper's main example of decoder-level passive watermarking.","marker":"[Fernandez et al., 2023]"},{"why":"Tree-Ring: embeds Fourier-space rings in the initial noise vector, the paper's exemplar for sampling-driven latent embedding.","marker":"[Wen et al., 2023]"},{"why":"RingID: extends Tree-Ring to multi-channel, multi-key identification and motivates the multi-attribution challenge.","marker":"[Ci et al., 2024]"},{"why":"WOUAF: modulates decoder layer parameters to embed watermark messages, illustrating effectiveness tuning in passive watermarking.","marker":"[Kim et al., 2024]"},{"why":"AdvDM: uses projected gradient descent to maximize LDM training loss, defining the adversarial proactive category.","marker":"[Liang et al., 2023]"},{"why":"Anti-DB: optimizes noise on surrogate models against DreamBooth, the basis for the proactive privacy-protection discussion.","marker":"[Van Le et al., 2023]"},{"why":"DiffPure: diffusion-based adversarial purification, the main attack that exposes watermark vulnerability.","marker":"[Nie et al., 2022]"},{"why":"GrIDPure: benchmarks fine-tuning attacks on watermarks and grounds the robustness challenge section.","marker":"[Zhao et al., 2024b]"}],"fun_headline_variants":["Diffusion watermarks: three camps, one built-in threat","Survey: diffusion models embed and erase watermarks","Watermarking meets diffusion: a three-camp taxonomy","Diffusion's double edge in watermark protection"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The survey's map is only as reliable as its characterizations of the cited methods; for example, defining passive watermarking as adding small noise to images misdescribes Tree-Ring, which modifies the initial latent's Fourier coefficients rather than adding noise to output pixels.","fun_headline_variants_meta":{"raw":{"variants":["Diffusion watermarks: three camps, one built-in threat","Survey: diffusion models embed and erase watermarks","Watermarking meets diffusion: a three-camp taxonomy","Diffusion's double edge in watermark protection"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000186,"raw_usage":{"total_tokens":1261,"prompt_tokens":818,"completion_tokens":443,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":434,"completion_tokens_details":{"reasoning_tokens":380}},"tokens_in":434,"tokens_out":443,"duration_ms":4950,"temperature":1.0,"reasoning_tokens":380,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T22:00:56.462344+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Inspect the primary sources behind the taxonomy: if a substantial share of the cited methods are not accurately described by the paper's category definitions, starting with whether Tree-Ring's watermark is in the initial latent's Fourier coefficients rather than in image pixels, then the taxonomy and the challenge list built on it do not hold as a map of the literature.","supporting_citations":[{"cited_title":"The stable signature: Rooting watermarks in latent diffusion models","cited_arxiv_id":null,"evidence_quote":"Stable Signature: fine-tunes an LDM decoder to embed a binary signature, the paper's main example of decoder-level passive watermarking."},{"cited_title":"Wouaf: Weight modulation for user attribution and fingerprinting in text- to-image diffusion models","cited_arxiv_id":null,"evidence_quote":"WOUAF: modulates decoder layer parameters to embed watermark messages, illustrating effectiveness tuning in passive watermarking."},{"cited_title":"Anti-dreambooth: Protecting users from personalized text-to-image synthesis","cited_arxiv_id":null,"evidence_quote":"Anti-DB: optimizes noise on surrogate models against DreamBooth, the basis for the proactive privacy-protection discussion."},{"cited_title":"Diffusion models for adversarial purification","cited_arxiv_id":null,"evidence_quote":"DiffPure: diffusion-based adversarial purification, the main attack that exposes watermark vulnerability."}],"review_version":1}