The only approved uses for SCP-3334-1 in encrypted form are the 
training and validation of machine learning models developed in 
Project MEDUSA. Any other access request regarding SCP-3334-1 
requires approval from the HMCL Supervisor for SCP-3334 or, in 
containment breach and MEDUSA failure events, the team lead for 
MTF Mu-4 (“Debuggers”). Direct access to unencrypted SCP-3334-1 
by personnel is prohibited 


Servers containing SCP-3334-1 are cordoned off, connected only to 
the local Site datacenter network. Every two weeks, the primary 
copy at Site-15 will be synchronized with its secondary replicas at 
Site-19 and Site-64 via physical delivery of storage media. 


SCP-3334-2 copies are to be stored on server disks at Sites 15, 19, 
49, 64, 77, 81. They are to be similarly physically synchronized once 
a week. Although Project MEDUSA is used throughout the 
Foundation in many of its tools, routines, and operations, 
SCP-3334-2 itself is only accessed locally due to Project MEDUSA’s 
Software as a Service (SaaS) centralized distribution model'. This 
means internal Foundation clients send requests to MEDUSA, which 
it then processes locally using SCP-3334-2 at one of the above 
Sites’ datacenters, preserving containment. 


Direct access to SCP-3334-2 is prohibited except for authorized 
personnel in the SCP-3334 containment team, Project MEDUSA 
staff, and approved project collaborators at the discretion of the 


SCP-3334 HMCL Supervisor. Temporary copies of SCP-3334-2 are 
allowed on local desktop computers for development in Project 
MEDUSA so long as standard protocols for working with visual 
memetic and cognitohazards (VMC hazards) are observed. 
Visualization of any element of SCP-3334-2 is prohibited. 


Foundation Scalable File System (FSFS) and scipDB: 
+ Brief Overview of Technical Aspects of SCP-3334 Containment 


Instances of SCP-3334-1 and SCP-3334-2 are saved on 
software tables in the scipDB system in order to ensure 
the security, integrity, and availability of SCP-3334-1. 
The scipDB system is a distributed noSQL 
multidimensional data map software intended to run 
across thousands of servers, developed in-house to 
store large amounts of sensitive or hazardous internal 
Foundation data2. It is a highly available, failure-tolerant, 
and scalable structured data storage system, achieving 
this with data replication across multiple servers to guard 
against data loss and increase throughput, gossip 
protocols to detect failures, and anti-entropy Merkle trees 
to recover from failures’. The special containment 
procedures for SCP-3334-1 prioritize security over 
availability and integrity, with some tolerance of 
SCP-3334-1 data loss or corruption to reduce possibility 
of unauthorized access - thus scipDB tables storing 
SCP-3334-1 are configured with a lower-than-default 
replication factor of 2 and some scipDB consistency 
features disabled. SCP-3334-2, the accuracy and 
availability of which is critical to Foundation operations, is 
stored with the standard replication factor and all 
consistency features enabled. 


The scipDB tables are built on top of files in the 
Foundation Scalable File System (FSFS), a distributed 
and decentralized file system optimized for data reads 
and appending writes* that suit Project MEDUSA’s 
typical workloads. The latter offers file-level 256-bit AES 
encryption, an option activated for all stored instances of 
SCP-3334 for prevention of unauthorized access°. 


Protocol 3334-10-Kempelen: 
+ Brief Overview of Protocol 3334-10-Kempelen 


Fifty D-class personnel are assigned to SCP-3334, with 
regular replacements as needed, to conduct Protocol 10- 
Kempelen. Functional eyesight, consciousness, and 
reasonable exposure to and knowledge of human culture 
and society are the only personnel requirements. The 
SCP-3334 containment team requests personnel that 
minimally satisfy these requirements that might 
otherwise not be useful in other Foundation projects, 
particularly subjects of previous accidents and testing, in 
the interests of efficiency. 


Protocol 10-Kempelen exposes human subjects under 
fMRI scanners to prospective visual memetic and 
cognitohazards to definitively verify their anomalous 
nature. Prospective VMC hazards can be flagged by 
Project MEDUSA or submitted by Foundation field 
teams. Verified anomalous VMC hazards are designated 
under SCP-3334-1 and used in Project MEDUSA. D- 
class personnel are administered amnestics after each 
10-Kempelen session. 


Attempts to dissolve Protocol 10-Kempelen and the use 
of D-class for these purposes by automating addition of 
training and validation data to SCP-3334-1 led to 
MEDUSA failure events, notably Incident 3334-1. 
Conversely, the scale of the Foundation's operations 
renders using D-class as a primary means of detecting 
VMC hazards impractical. Thus the protocol was 
preserved in its current ancillary purpose of manually 
verifying potential training and validation data for use in 
Project MEDUSA. 


Project MEDUSA: 
+ Brief Overview of Project MEDUSA 


Project MEDUSA is an internal Foundation effort by the 


Department of Analytics to build an automated system to 
detect visual memetic and cognitohazards (VMC 
hazards) using non-anomalous, understood machine 
learning techniques. 


Project MEDUSA is currently used across many 
Foundation tools, routines, and operations that require 
the detection of VMC hazards. These include the 
command-line memescan utility, the Anansi, Shelob, and 
Aragog Foundation web-crawlers, the Giulianna image 
analysis software, SCRAMBLE goggles, [REDACTED], 
and in the containment of numerous SCP anomalies. 


Project MEDUSA uses advanced machine learning 
algorithms (currently, an ensemble of recurrent deep-Q 
neural networks). At a high level, the algorithm learns by 
taking in labelled training examples, in this case, of VMC 
hazards from SCP-3334-1 and normal pictures, and 
modifying itself to be able to distinguish between them. 
After training, it can take in new examples and then 
predict whether they are VMC hazards or not. In this 
sense, it is a “weak Al”, able to improve itself at a 
specific task without any notion of a conscious. Project 
MEDUSA does not employ or develop sentient Als, 
known as “strong Al” - such research falls outside the 
purview of the project. 


+ Brief Overview of Technical Aspects of Project MEDUSA 


Initial efforts to identify cognitohazards with artificial 
intelligence techniques focused largely on support vector 
machines§, until convolutional neural networks’ vastly 
improved classification accuracy8. Addition of long-short 
term memory (LSTM) layers to make the networks 
recurrent allowed for analysis of video and non-static 
visual cognitohazards9. 


Detection of memetic hazards proved more difficult, 
since it required the algorithms to not just identify, but 
also understand the content of images and their 
conceptual relationships. Combined with past research, 


however, deep reinforcement learning! eventually 
achieved this!1. The theoretical flexibility of deep 
reinforcement learning enabled the combination of 
detecting both visual memetic and cognitohazards under 
a single deep neural network, rather than in two separate 
narrow programs - this development led to the formation 
of Project CASSANDRA, which eventually became 
Project MEDUSA (see Addendum 3334-1). 


MEDUSA currently uses an ensemble of recurrent deep- 
Q neural networks, since ensembles reduce variance 
and expected generalization error and thus improve real 
world performance. Each network in the ensemble 
branches into two sub-networks as indicated in the 
diagram: a policy network and a value network. The 
policy network has 40 layers while the value network has 
30, with dropout used as regularization. The networks 
are trained using an $\epsilon$-greedy training strategy 
with exploration and exploitation phases as $\epsilon$ 
anneals, while weights are updated using stochastic 
gradient descent and backpropagation. Further 
hyperparameter specifications are available on a need- 
to-know basis. 


As a result of Incident 3334-1, inputs are preprocessed 
using principal components analysis and a cascade 
classifier to determine and remove adversarial examples 
and prevent malicious manipulation or deterioration of 
the MEDUSA model. 


+ Brief Overview of Project MEDUSA Procedures 


Any proposed minor modifications to arbitrary MEDUSA 
model hyperparameters, including but not limited to: 
learning rate, loss function, activation functions, learning 
rate decay/momentum parameter, weight initialization, 
dropout regularization, or neural net structure, should be 
proposed to the Project MEDUSA Tuning Team for 
review and approval. The Tuning Team currently uses 
auto-tuning algorithms to determine most of these 
hyperparameters. 


SCP-432: Cabinet Maze 


Item #: SCP-432 
Object Class: Safe 


Special Containment Procedures: SCP-432 is kept in a standard 
storage area at Sector-25, is to be kept locked at all times and the 
key to the lock kept in the adjacent security station under guard by 
three (3) Level 3 Personnel. No other special containment required. 


Description: SCP-482 is a 2-door steel storage cabinet, measuring 
2 meters tall by 1.2 meters wide by 1 meter deep. The exterior of the 
cabinet is painted matte green and bears no remarkable features 
except small areas of corrosion and light scratching commensurate 
with being left exposed to the elements for a prolonged period of 
time. The doors of the cabinet are fitted with a basic slide-bolt and a 
hasp for a padlock, allowing the door to be secured from outside. 


The interior dimensions of SCP-432 display significant disparity with 
the exterior; the doors open into an apparently extradimensional 
space containing a large labyrinth complex comprised of an as-yet 
uncharted series of corridors. The walls, floor and ceiling of the 
corridors are constructed from heavily-rusted steel and adhere to the 
same height and width scales as the exterior of SCP-432 (2 m high 
by 1.2 m wide). 


The corridors within SCP-482 are lit at irregular intervals by what 
appear to be regular household lightbulbs, secured to the walls in 
wire mesh fittings. Many of the bulbs are observed to flicker and 
numerous others are burned out or broken. In places several large- 
gauge steel pipes have been found bolted to the walls of the 
tunnels; these pipes are notably cold to the touch and contain 
flowing water, although the source and destination of the pipes and 
water are unknown. Many of the pipes observed are in obvious need 
of repair and leak cold (average of 3°C) water. Analysis of this water 
has revealed a low oxygen content and trace amounts of iron oxide 


Any major suggested changes to the underlying 
MEDUSA model algorithm should be submitted in a 
formal written proposal to the Project MEDUSA director. 
Such amendments will be reviewed by all major Project 
MEDUSA team leads and will require formal 
mathematical verification with provable confidence 
bounds on generalization error, full regression testing, 
and a 10-folds cross-validation accuracy check using 
data from SCP-3334-1 before entering the official 
implementation. 


Every two weeks, the current MEDUSA machine learning 
model is retrained on new training data from SCP-3334-1 
in order to keep it up to date with the latest VMC 
hazards. Further, the Project MEDUSA team will perform 
full regression testing on the model, including running it 
on a validation dataset taken from SCP-3334-1 with a 
required 99.9% correct classification accuracy required 
to pass. If the new model passes, then SCP-3334-2 will 
be updated and backed up appropriately, while the last 
known functional commit will be tagged as such on the 
Foundation’s internal codebase version control system. 
Any additional revalidation of the model beyond this 
biweekly basis is subject to Project MEDUSA director 
approval to prevent overfitting of the model and 
deteriorated real-world performance. 


The performance of the active MEDUSA model is 
monitored for real-world accuracy. Preferable operational 
accuracy would be maintained at 99.9%. If accuracy dips 
below 90%, a MEDUSA failure event is declared. In this 
case, the model’s parameters are reverted to the last 
known functional version of SCP-3334-2 and the 
codebase is reverted to the last tagged commit. If the 
issue is not immediately resolved, the Project MEDUSA 
team should request MTF Mu-4 (“Debuggers’”). In the 
event of a prolonged outage, the SCP-3334 containment 
team can request a ramp-up of Protocol 10-Kempelen of 
up to 1000 D-class personnel as a temporary 
replacement for Project MEDUSA. However, given the 


volume and time-sensitivity of VMC hazard detection 
needs across Foundation operations, as well as the near 
ubiquity and speed of the internet in public life, Project 
MEDUSA is a critical infrastructure component and the 
potential consequences of its indefinite interruption are 
unknown. The loss or inadequacy of Foundation 
automatic VMC hazard detection capabilities could 
potentially lead to an LV-O Lifted Veil scenario, or even 
one of various K-class end-of-the-world scenarios in the 
event of a major breach or outbreak of anomalous VMC 
hazards. 


Description: SCP-3334 is a designation for various anomalous data 
necessary to implement Project MEDUSA. 


SCP-3334-1 is a collection of 1564288059 gathered anomalous 
visual memetic and cognitohazards (VMC hazards), collected 
through Protocol 10-Kempelen. This dataset is also artificially 
enlarged using data augmentation techniques, including 
transformations and translations of the original VMC hazards. 
Approximately 90% of SCP-3334-1 is designated as training data, 
used as examples to train machine learning models in Project 
MEDUSA. The remaining 10% is reserved as validation data, used 
to anticipate real-world accuracy during testing. Individual images 
are identified as SCP-3334-1-# as appropriate. 


SCP-3334-2 is the numerical internal weights used by the neural 
network models in Project MEDUSA. These weights determine how 
the neural nets classify given input images as hazardous or not, and 
are modified by the neural net during training and learning. Recent 
results in machine learning research indicate the learning of 
hierarchical representations within intermediate layers of 
convolutional neural networks!2, justifying the designation and 
containment of SCP-3334-2 as potential visual memetic/ 
cognitohazards. 


+ Addendum 3334-1 


Addendum 3334-1: As of / /20 , in light of recent 
literature regarding new techniques in deep 
reinforcement learning and their ability to unify multiple 


kinds of visual hazard classification!3, the Director of the 
Department of Analytics has ordered the unification of 
Projects CIRCE and ODIN into a single Project 
CASSANDRA that will develop an automatic detection 
system for both visual memetic and cognitohazards. 
Their substantial collections of VMC hazards, formerly 
scattered throughout the SCP main database or 
anomalous object lists, were combined into a single set 
and granted the shared designation SCP-3334. 


+ Addendum 3334-2 


Addendum 3334-2: On / /20 ,a major containment 
breach involving SCP- at Site-15 resulted in 
researcher deaths or incapacitations, of whom were 
assigned or otherwise attached to Project CASSANDRA 
or SCP-3334 containment. Project CASSANDRA Testing 
Team lead Dr. Tourres was unaccounted for. The drastic 
loss in qualified personnel resulted in an unprecedented 
Foundation recruiting drive from external companies and 
universities to recover the lost human capital and 
technical talent.